SUN Attribute Database: Discovering, Annotating, and Recognizing

SUN Attribute Database: Discovering, Annotating, and Recognizing
SUN Attribute Database:
Discovering, Annotating, and Recognizing Scene Attributes
Genevieve Patterson and James Hays
Brown University
In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human
studies to find a taxonomy of 102 discriminative attributes.
Next, we build the “SUN attribute database” on top of the
diverse SUN categorical database. Our attribute database
spans more than 700 categories and 14,000 images and
has potential for use in high-level scene understanding and
fine-grained scene recognition. We use our dataset to train
attribute classifiers and evaluate how well these relatively
simple classifiers can recognize a variety of attributes related to materials, surface properties, lighting, functions
and affordances, and spatial envelope properties.
Figure 1: Visualization of a hypothetical space of scenes embedded in 2D and partitioned by categories. Categorical scene
representations have several potential shortcomings: (1) Important intra-class variations such as the dramatic differences between
four “village” scenes can not be captured, (2) hard partitions break
up the continuous transitions between many scene types such as
“forest” and “savanna”, (3) an image can depict multiple, independent categories such as “beach” and “village”, and (4) it is difficult to reason about unseen categories, whereas attribute-based
representations lend themselves towards zero-shot learning [17].
1. Introduction
Scene representations are vital to enabling many datadriven graphics and vision applications. There is important research on low-level representations of scenes (i.e.
visual features) such as the gist descriptor [14] or spatial
pyramid [12], but there has been little investigation into
high-level representations of scenes (e.g. attributes or categories). The standard category-based recognition paradigm
has gone largely unchallenged. In this paper, we explore a
new, attribute-based representation of scenes.
Traditionally, computer vision algorithms describe visual phenomena (e.g. objects, faces, actions, scenes, etc.)
by giving each instance a categorical label (e.g. cat, Halle
Berry, drinking, downtown street, etc.). For scenes, this
model has several significant issues, visualized in Figure 1:
(1) The extent of scene understanding achievable is quite
shallow – there is no way to express interesting intracategory variations. (2) The space of scenes is continuous,
so hard partitioning creates numerous ambiguous boundary
cases. (3) Images often simultaneously exhibit characteristics of multiple distinct scene categories. (4) A categorical
representation can not generalize to types of scenes which
were not seen during training.
In the past several years there has been interest in
attribute-based representations of objects [7, 10, 6, 5, 1,
18, 21], faces [9], and actions [24, 13] as an alternative or
complement to category-based representations. However,
there has been only limited exploration of attribute-based
representations for scenes, even though scenes are uniquely
poorly served by categorical representations. For example,
an object usually has unambiguous membership in one category. One rarely observes issue 2 (e.g. this object is on
the boundary between sheep and horse) or issue 3 (e.g. this
object is both a potted plant and a television).
In the domain of scenes, an attribute-based representation might describe an image with “concrete”, “shopping”,
“natural lighting”, “glossy”, and “stressful” in contrast to
a categorical label such as “store”. Figure 2 visualizes the
space of scenes partitioned by attributes rather than categories. Note, the attributes do not follow category boundaries. Indeed, that is one of the appeals of attributes – they
can describe intra-class variation (e.g. a canyon might have
ning more than 700 categories and 14,000 images (Section 3). We visualize the distribution of scenes in attribute
space (Section 4) and measure how well our attributes predict scene category (Section 5). Finally we measure how
accurately we can predict attributes using existing image
representations (Section 6).
2. Building a Taxonomy of Scene Attributes
from Human Descriptions
Figure 2: Hypothetical space of scenes partitioned by attributes
rather than categories. In reality, this space is much higher dimensional and there are not clean boundaries between attribute
presence and absence.
water or it might not) and inter-class relationships (e.g. both
a canyon and a beach could have water).
A small set of scene attributes were explored in Oliva
and Torralba’s seminal “gist” paper [14] and follow-up
work [15]. Eight “spatial envelope” attributes were found
by having participants manually partition a database of
eight scene categories. These attributes such as openness,
perspective, and depth were predicted based on the scene
gist representation. In [8], it was shown that these global
scene attributes are predictive of human performance on a
rapid basic-level scene categorization task.
More recently, Parikh and Grauman [16] argue for “relative” rather than binary attributes. They demonstrate results
on the eight category outdoor scene database, but their training data is limited – they do not have per-scene attribute
labels and instead provide attribute labels at the category
level (e.g. all highway scenes should be more “natural” than
all street scenes). This undermines one of the potential advantages of attribute-based representations – the ability to
describe intra-class variation. In this paper we discover, annotate, and recognize 15 times as many attributes using a
database spanning 90 times as many categories where every
scene has independent attribute labels.
Paper Outline. This paper primarily focuses on the creation and verification of our SUN attribute database in the
spirit of analogous database creation efforts such as ImageNet [3], LabelMe [19], and Tiny Images [22]. First,
we derive a taxonomy of more than 100 scene attributes
from crowd-sourced experiments (Section 2). Next, we use
crowd-sourcing to construct our attribute-labeled dataset on
top of a significant subset of the SUN database [23] span-
Our first task is to establish a taxonomy of scene attributes for further study. The space of attributes is effectively infinite but the majority of possible attributes (e.g.,
“Was this photo taken on a Tuesday”, “Does this scene contain air?”) are not interesting. We are interesting in finding discriminative attributes which are likely to distinguish
scenes from each other (not necessarily along categorical
boundaries). We limit ourselves to global, binary attributes.
This limitation is primarily economic – we collect millions
of labels and annotating binary attributes is more efficient
than annotating real-valued or relative attributes. None-theless, by averaging the binary labels from multiple annotators we produce a real-valued confidence for each attribute.
To determine which attributes are most relevant for describing scenes we perform open-ended image description
tasks on Amazon Mechanical Turk (AMT). First we establish a set of “probe” images for which we will collect descriptions. We want a set of images which is maximally
diverse and representative of the space of scenes. For this
reason we use the images which human participants found
to be most typical of 707 SUN database categories [4]. We
first ask AMT workers to provide text descriptions of individual scenes. From thousands of such tasks (hereafter
HITs, for human intelligence tasks) it emerges that people
tend to describe scenes with five types of attributes: (1) Materials (e.g. cement, vegetation), (2) surface properties (e.g.
rusty) (3) functions or affordances (e.g. playing, cooking),
(4) spatial envelope attributes (e.g. enclosed, symmetric),
and (5) object presence (e.g. cars, chairs).
Within these broad categories we focus on discriminative attributes. To find such attributes we develop a simplified, crowd-sourced version of the “splitting task” used
by [14]. We show AMT workers two groups of scenes
and ask them to list attributes of each type (material, surface property, affordance, spatial envelope, and object) that
are present in one group but not the other. The images that
make up these groups are typical scenes from distinct, random categories. In the simplest case, with only one scene
in each set, we found that participants would focus on trivial, happenstance objects or attributes (e.g. “treadmill” or
“yellow shirt”). Such attributes would not be broadly useful for describing other scenes. At the other extreme, with
many category prototypes in each set, it is rare that any attribute would be shared by one set and absent from the other.
We found that having two random scene prototypes in each
set elicited a diverse, broadly applicable set of attributes.
Figure 3 shows
The attribute
gathering task
was repeated
times. From
Figure 3: Mechanical Turk interface for
discovering discriminative attributes.
the thousands
of raw discriminative attributes reported by participants we collapse
nearly synonymous responses (e.g. dirt and soil) into
single attributes. We omit attributes related to aesthetics
rather than scene content. For this study we also omit the
object presence attributes from further discussion because
prediction of object presence, i.e. object classification,
has been thoroughly investigated (Additionally, the SUN
database already has dense object labels for most scenes).
Our participants did not report all of the spatial envelope
attributes found by [14], so we manually add binary versions of those attributes so that our taxonomy is a superset
of prior work. In total, we find 38 material, 11 surface
property, 36 function, and 17 spatial envelope attributes.
3. Building the SUN Attribute Database
With our taxonomy of attributes finalized we create the
first large-scale database of attribute-labeled scenes. We
build the SUN attribute database on top of the existing SUN
categorical database [23] for two reasons: (1) to study of the
interplay between attribute-based and category-based representations and (2) to ensure a diversity of scenes. We annotate 20 scenes from each of the 717 SUN categories that
contain at least 20 instances. Each scene has 102 attributes
and each attribute will have multiple annotations. In total
we gather more than four million labels. This necessitates a
crowdsourced annotation strategy and we once again utilize
3.1. The Attribute Annotation Task
The primary difficulty of using a large, non-expert workforce is ensuring that the collected labels are accurate while
keeping the annotation process fast and economical [20].
From an economic perspective, we want to have as many
images labeled as possible for the lowest price. From
a quality perspective, we want workers to easily and accurately label images. We find that particular UI design
decisions and worker instructions significantly impacted
throughput and quality of results. After several iterations,
we choose a design where workers are presented with a grid
of 4 dozen images and are asked to consider only a single
attribute at a time. Workers click on images which exhibit
Figure 4: Annotation interface for AMT workers. The particular attribute being labeled is prominently shown and defined. Example scenes which contain the attribute are shown. The worker
can not scroll these definitions or instructions off of their screen.
When workers mouse over a thumbnail a large version appears in
the preview window in the top right corner.
the attribute in question. Before working on our HITs, potential annotators are required to pass a quiz covering the
fundamentals of attribute identification and image labeling.
An example of our HIT user interface is shown in Figure 4.
Even after the careful construction of the annotation interface and initial worker screening, many workers’ annotations are unreasonable. We use several techniques to filter
out bad workers and then cultivate a pool of trusted workers:
Filtering bad workers. Deciding whether or not an attribute is present in a scene image is sometimes an ambiguous task. This ambiguity combined with the financial incentive to work quickly leads to sloppy annotation from some
workers. In order to filter out those workers who performed
poorly, we flag HITs which are outliers with respect to annotation time or labeling frequency. Some attributes, such
as “ice” or “fire”, rarely appear and are visually obvious and
thus those HITs can be completed quickly. Other attributes,
such as “man-made” or “natural light”, occur in more than
half of all scenes thus the expected completion time per HIT
is higher. Any worker whose average number of labels or
work time for a given attribute is greater than one standard
deviation away from the average for all workers is added to
a list of workers to manually review. We review by hand a
(a) The total number of labels com- (b) The average time (sec) each
pleted by each worker.
worker spent on a HIT of image labels.
Figure 5: These plots visualize our criteria for identifying suspicious workers to grade. Figure 5a shows the heavy-tailed distribution of worker contributions to the database. The top workers
spent hundreds of hours on our HITs. The red line in plot 5b demarcates the average work time across all workers, and the blue
lines mark the positive and negative standard deviation from the
mean. Work time statistics are particularly useful from identifying
scam workers as they typically rush to finish HITs.
fraction of the HITs for each suspicious worker as well as
a random sampling of non-suspicious workers. Any worker
whose annotations are clearly wrong is added to a blacklist.
They are paid for their time, but none of their labels become
part of the final dataset.
Cultivating good workers. The pay per HIT is initially
$0.03 but increases to $0.05 plus 10% bonus after workers
have a proven track record of accuracy. The net result of
our filtering and bonus scheme is that we cultivate a pool
of trained, efficient, and accurate annotators as emphasized
by [2]. In general, worker accuracy rose over time and
we omit over one million early annotations from the final
After labeling the entire dataset once with the general
AMT population, we identify a smaller group of 38 trusted
workers out of the ∼800 who participated. We repeat the
labeling process two more times using only these trusted
workers. The idea of finding and heavily utilizing good
workers is in contrast to the “wisdom of the crowds” crowdsourcing strategy where consensus outweighs expertise, but
is consistent with recent research such as [11] where good
workers were shown to be faster and more accurate than the
average of many workers. Figure 5 shows the contributions
of all workers to our database.
Figure 6 qualitatively shows the result of our annotation process. To quantitatively assess accuracy we manually grade ∼600 random positive and ∼600 random negative AMT annotations in the database. For both types of
annotation, we find ∼93% of labels to be reasonable. Negative labels are more common, making up 92% of the annotations. This does not seem to be an artifact of our interface
(which defaults to negative), but rather it seems that scene
attributes follow a heavy-tailed distribution with a few being very common (e.g. “natural”) and most being rare (e.g.
In the following sections, our experiments rely on the
consensus of multiple annotators rather than individual annotations. This increases the accuracy of our labels. We
manually grade 5 scenes for each of our 102 attributes
where the consensus was positive (2 or 3 votes) and likewise for negative (0 votes). In these 1020 scenes we find
that the consensus annotation is reasonable 95% of the time
for both positive and negative labels.
4. Exploring Scenes in Attribute Space
Now that we have a database of attribute-labeled scenes
we can attempt to visualize that space of attributes. In
Figure 7 we show all 14,340 of our scenes projected onto
the two highest variance PCA bases. We sample several
points in this space to show the types of scenes present as
well as the nearest neighbors to those scenes in attribute
space. For this analysis the distance between scenes is simply the Euclidean distance between their real-valued, 102dimensional attribute vectors.
5. Predictive Power of Attributes
In this section we measure how well we can predict
scene category from ground truth scene attributes. While
the goal of this paper and our database is not necessarily
to improve the task of scene categorization, this analysis
does give some insight into the interplay between scene categories and scene attributes.
One hundred binary attributes could potentially predict
membership in 700 hundred categories if the attributes were
(1) independent and (2) consistent within each category,
but neither of these are true. Many of the attributes are
correlated (e.g. “farming” and “open area”) and there is
significant attribute variation within categories. Furthermore, many groups of SUN database scenes would require very specific attributes to distinguish them (e.g. “forest needleleaf” and “forest broadleaf”), so it would likely
take several hundred attributes to very accurately predict
scene categories.
Figure 8 shows how well we can predict the category of
a scene with known attributes as we increase the number of
training examples per category. Each image is represented
by the ground truth average attribute labels with no visual
features. We compare this to the classification accuracy of
visual features [23] on the same data set. With 1 training example per category, attributes are roughly twice as accurate
as visual features. Performance equalizes as the number of
training examples approaches 20 per category. The performance of our attribute-based classifiers hints at the viability
of zero-shot learning techniques which have access to attribute distributions for categories but no visual examples.
The fact that category prediction accuracy increases signif-
Figure 6: The images in the table above are grouped by the number of positive labels (votes) they received from AMT workers. From
left to right the visual presence of each attribute increases. Note that for functional / affordance attributes, AMT workers are instructed to
positively label an image if the attribute is likely to occur in that image, not just if it is actually occurring.
6. Recognizing Scene Attributes
395 category SUN Attribute dataset
717 category SUN Attribute dataset
397 category SUN dataset, combined features classifier [28]
Recognition Rate
Number training samples per class
Figure 8: Category recognition from ground truth attributes using an SVM. We plot accuracy for the 717 category SUN Attribute
dataset and for a subset of 395 categories which roughly match
the evaluation of the SUN 397 dataset [23] (two categories present
in [23] are not part of the SUN Attribute dataset). We compare
attribute-based recognition to visual recognition by plotting the
highest accuracy from [23] (pink dotted line).
icantly with more training examples may be a reflection of
intra-class attribute variations.
A motivation for creating the SUN Attribute dataset is to
enable deeper understanding of scenes. For scene attributes
to be useful they need to be machine recognizable. To assess the difficulty of scene attribute recognition we perform
experiments using the features and kernels which achieve
state of the art category recognition on the 15 scene database
and SUN database. Xiao et al. in [23] show that a combination of several scene descriptors results in a significantly
more powerful classifier than any individual feature. Accordingly, our SVM classifiers use a combination of kernels
generated from gist, HOG 2x2, self-similarity, and geometric context color histogram features (See [23] for feature
and kernel details). These four features were chosen because they are each individually powerful and because they
can describe distinct visual phenomena.
To recognize attributes in images, we create an individual classifier for each attribute using random splits of the
SUN Attribute dataset for training and testing data. Note
that our training and test splits are category agnostic – for
the purpose of this section we simply have a pool of 14,340
images with varying attributes. We treat an attribute as
present if it receives at least two votes and absent if it receives zero votes. As shown in Figure 6, images with a
Figure 7: 2D visualization of the SUN Attribute dataset. Each image in the dataset is represented by the projection of its 102-dimensional
attribute feature vector onto two PCA dimensions. There are 7 groups of nearest neighbors, each designated by a color. The centers of the
nearest neighbor groups are marked with a square. Interestingly, while the nearest-neighbor scenes in attribute space are semantically very
similar, for most of these examples (underwater ocean, abbey, coast, ice skating rink, field wild) none of the nearest neighbors actually fall
in the same SUN database category. The colored border lines deliniate the approximate separation of images with and without the attribute
associated with the border. Figure best viewed in color.
single vote tend to be in a transition state between the attribute being present or absent so they are excluded from
these experiments. We train and evaluate independent classifiers for each attribute even though correlation between
attributes could make “multi-label” classification methods
advantageous. For each attribute we wish to recognize we
train an SVM with each of our four features. We calculate
the average precision (AP) of each classifier and construct
a combined kernel from a combination of individual feature
kernels. Each kernel is normalized and then weighted in
proportion to that feature’s AP for a given attribute. Each
classifier is trained on 300 images and tested on 50 images
and AP is computed over five random splits. Each classifier’s train and test sets are half positive and half negative
even though most attributes are sparse (i.e. usually absent).
We fix the positive to negative ratio so that we can compare
the intrinsic difficulty of recognizing each attribute without
being influenced by attribute popularity.
Figure 9 shows that the average performance of our classifiers is fairly good (AP .88) and the combined classifier
Figure 9: Average Precision values averaged for all attributes.
The combined feature classifier is more accurate than any individual feature classifier. Average Precision steadily increases with
more training data.
Figure 10: Average Precision for all of the attributes in our dataset. The AP of chance selection is marked by the red line. All attributes
can be recognized significantly better than chance, even when the visual manifestation of such attributes tends to be quite subtle.
outperforms any individual feature. Not all attributes are
equally easy to recognize – Figure 10 plots the average precision for each attribute’s combined feature SVM. It is clear
from Figure 10 that certain attributes, especially some surface properties and spatial envelope attributes, are particularly difficult to recognize with our global image features.
We show qualitative results of our attribute classifiers in
Figure 11. Our attribute classifiers perform well at recognizing attributes in a variety of contexts. Most of the attributes with strong confidence are indeed present in the
images. Likewise, the lowest confidence attributes are
clearly not present. It is particularly interesting that function/affordance attributes and surface property attributes are
often recognized with the stronger confidence than other
types of attributes even though functions and surface properties are complex concepts that may not be easy to define
visually. For example the golf course test image in Figure 11 shows that our classifiers can successfully identify
such abstract concepts as “sports” and “competing” for a
golf course, which is visually quite similar to places where
no sports would occur. Abstract concepts such as “praying”
and “aged/worn” are also recognized correctly in both the
abbey and mosque scenes in Figure 11. Figure 12 shows
three failure cases.
7. Discussion
Scene attributes are a fertile, unexplored recognition domain. Many attributes are visually quite subtle and nearly
all scene descriptors in the literature were developed for the
task of scene categorization and may not generalize well
to attribute recognition. Even though all of our attribute
labels are global, many attributes have clear spatial support (materials) while others may not (functions and affordances). Techniques from weakly supervised object recognition might have success at discovering the spatial support
of our global attributes where applicable. Multi-label clas-
Test Scene Images
Detected Attributes
Most Confident Attributes: vegetation,
open area, sunny, sports, natural light,
no horizon, foliage, competing, railing,
Least Confident Attributes: studying,
gaming, fire, carpet, tiles, smoke, medical, cleaning, sterile, marble
Most Confident Attributes: shrubbery,
flowers, camping, rugged scene, hiking,
dirt/soil, leaves, natural light, vegetation, rock/stone
Least Confident Attributes: shingles,
ice, railroad, cleaning, marble, sterile,
smoke, gaming, tiles, medical
Most Confident Attributes: eating, socializing, waiting in line, cloth, shopping, reading, stressful, congregating,
man-made, plastic
Least Confident Attributes: gaming,
running water, tiles, railroad, waves/
surf, building, fire, bathing, ice, smoke
Most Confident Attributes: vertical components, vacationing, natural light, shingles, man-made, praying, symmetrical,
semi-enclosed area, aged/ worn, brick
Least Confident Attributes: railroad, ice,
scary, medical, shopping, tiles, cleaning,
sterile, digging, gaming
Most Confident Attributes: vertical components, brick, natural light, praying, vacationing, man-made, pavement,
sunny, open area, rusty
Least Confident Attributes: ice, smoke,
bathing, marble, vinyl, cleaning, fire,
tires, gaming, sterile
Figure 11: Attribute detection. For each query, the most confidently recognized attributes (green) are indeed present in the test
images, and the least confidently recognized attributes (red) are
either the visual opposite of what is in the image or they are irrelevant to the image.
Test Images
Detected Attributes
Most Confident Attributes: swimming,
asphalt, open area, sports, sunbathing,
natural light, diving, still water, exercise, soothing
Least Confident Attributes: tiles, smoke,
ice, sterile, praying, marble, railroad,
cleaning, medical activity, gaming
Most Confident Attributes: cold, concrete, snow, sand, stressful, aged/ worn,
dry, climbing, rugged scene, rock/stone
Least Confident Attributes: medical
activity, spectating, marble, cleaning,
waves/ surf, railroad, gaming, building,
shopping, tiles
Most Confident Attributes: carpet, enclosed area no horizon, electric/indoor
lighting, concrete, glossy, cloth, working, dry, rubber/ plastic
Least Confident Attributes: trees, ocean,
digging, open area, scary, smoke, ice,
railroad, constructing/ building, waves/
Figure 12: Failure cases. In the top image, it seems the smooth,
blue regions of the car appear to have created false positive detections of “swimming”, “diving”, and “still water”. The bottom images, unlike all of our training data, is a close-up object view rather
than a scene with spatial extent. The attribute classifiers seem to
interpret the cat as a mountain landscape and the potato chips bag
as several different materials - “carpet”, “concrete”, “glossy”, and
sification methods, which exploit the correlation between
attributes, might also improve accuracy when recognizing
attributes simultaneously. We hope that the scale and variety of our dataset will enable many future explorations in
the exciting space of visual attributes.
We thank Vazheh Moussavi
(Brown Univ.) for his insights and contributions in the
data annotation process. Genevieve Patterson is supported
by the Department of Defense (DoD) through the National
Defense Science & Engineering Graduate Fellowship (NDSEG) Program. This work is also funded by NSF CAREER
Award 1149853 to James Hays.
[1] T. Berg, A. Berg, and J. Shih. Automatic Attribute Discovery
and Characterization from Noisy Web Data. ECCV, pages
663–676, 2010. 1
[2] D. Chen and W. Dolan. Building a persistent workforce on
mechanical turk for multilingual data collection. In The 3rd
Human Computation Workshop (HCOMP), 2011. 4
[3] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei.
ImageNet: A Large-Scale Hierarchical Image Database. In
CVPR, 2009. 2
[4] K. A. Ehinger, J. Xiao, A. Torralba, and A. Oliva. Estimating
scene typicality from human ratings and image features. In
33rd Annual Conference of the Cognitive Science Society,
2011. 2
[5] I. Endres, A. Farhadi, D. Hoiem, and D. Forsyth. The Benefits and Challenges of Collecting Richer Object Annotations.
In ACVHL 2010 (in conjunction with CVPR)., 2010. 1
[6] A. Farhadi, I. Endres, and D. Hoiem. Attribute-centric recognition for cross-category generalization. In CVPR, 2010. 1
[7] A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing
objects by their attributes. In CVPR, 2009. 1
[8] M. Greene and A. Oliva. Recognition of natural scenes from
global properties: Seeing the forest without representing the
trees. Cognitive psychology, 58(2):137–176, 2009. 2
[9] N. Kumar, A. Berg, P. Belhumeur, and S. Nayar. Attribute
and simile classifiers for face verification. In ICCV, 2009. 1
[10] C. H. Lampert, H. Nickisch, and S. Harmeling. Learning To
Detect Unseen Object Classes by Between-Class Attribute
Transfer. In CVPR, 2009. 1
[11] Lasecki, Murray, White, Miller, and Bigham. Real-time
Crowd Control of Existing Interfaces. In UIST, 2011. 4
[12] S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural
Scene Categories. In CVPR, 2006. 1
[13] J. Liu, B. Kuipers, and S. Savarese. Recognizing Human
Actions by Attributes. In CVPR, 2011. 1
[14] A. Oliva and A. Torralba. Modeling the shape of the scene:
A holistic representation of the spatial envelope. IJCV,
42(3):145–175, 2001. 1, 2, 3
[15] A. Oliva and A. Torralba. Scene-Centered Description from
Spatial Envelope Properties. In 2nd Workshop on Biologically Motivated Computer Vision (BMCV), 2002. 2
[16] D. Parikh and K. Grauman. Interactively Building a Discriminative Vocabulary of Nameable Attributes. In CVPR, 2011.
[17] D. Parikh and K. Grauman. Relative Attributes. In ICCV,
2011. 1
[18] O. Russakovsky and L. Fei-Fei. Attribute learning in
largescale datasets. In ECCV 2010 Workshop on Parts and
Attributes, 2010. 1
[19] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. LabelMe: a Database and Web-based Tool for Image
Annotation. IJCV, 77(1-3), 2008. 2
[20] A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. In First IEEE Workshop on Internet
Vision at CVPR 08, 2008. 3
[21] Y. Su, M. Allan, and F. Jurie. Improving Object Classification using Semantic Attributes. In BMVC, 2010. 1
[22] A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny
images: a large dataset for non-parametric object and scene
recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 2008. 2
[23] J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. SUN
database: Large-scale scene recognition from abbey to zoo.
In CVPR, 2010. 2, 3, 4, 5
[24] B. Yao, X. Jiang, A. Khosla, A. L. Lin, L. Guibas, and L. FeiFei. Human Action Recognition by Learning Bases of Action Attributes and Parts. In ICCV, 2011. 1
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