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Author |
Naila Murray; Luca Marchesotti; Florent Perronnin |
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Title |
AVA: A Large-Scale Database for Aesthetic Visual Analysis |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
2408-2415 |
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Abstract |
With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks |
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Providence, Rhode Islan |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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CIC |
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no |
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Call Number |
Admin @ si @ MMP2012a |
Serial |
2025 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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Title |
Names and Shades of Color for Intrinsic Image Estimation |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Volume |
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Pages |
278-285 |
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In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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CIC |
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no |
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Call Number |
Admin @ si @ SPB2012 |
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2026 |
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Author |
Murad Al Haj; Jordi Gonzalez; Larry S. Davis |
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Title |
On Partial Least Squares in Head Pose Estimation: How to simultaneously deal with misalignment |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
2602-2609 |
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Abstract |
Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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ISE |
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no |
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Call Number |
Admin @ si @ HGD2012 |
Serial |
2029 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
749-756 |
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Abstract |
Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
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Providence, Rhode Island |
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IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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CVPR |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RSL2012b; ADAS @ adas @ |
Serial |
2033 |
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Permanent link to this record |
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Author |
Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny |
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Title |
Leveraging category-level labels for instance-level image retrieval |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
3045-3052 |
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Abstract |
In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results. |
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Address |
Providence, Rhode Island |
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Publisher |
IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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Conference |
CVPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GRP2012 |
Serial |
2050 |
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Permanent link to this record |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Document classification using multiple views |
Type |
Conference Article |
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Year |
2012 |
Publication |
10th IAPR International Workshop on Document Analysis Systems |
Abbreviated Journal |
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Volume |
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Pages |
33-37 |
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Abstract |
The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task. |
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Australia |
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Publisher |
IEEE Computer Society Washington |
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ISBN |
978-0-7695-4661-2 |
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DAS |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GPV2012 |
Serial |
2049 |
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Permanent link to this record |
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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
Stable Salient Shapes |
Type |
Conference Article |
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Year |
2012 |
Publication |
International Conference on Digital Image Computing: Techniques and Applications |
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DICTA |
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Notes |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ MCG2012b |
Serial |
2166 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez |
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Title |
Road Scene Segmentation from a Single Image |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7578 |
Issue |
VII |
Pages |
376-389 |
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Keywords |
road detection |
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Abstract |
Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined |
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Address |
Florence, Italy |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33785-7 |
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Conference |
ECCV |
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Notes |
ADAS;ISE |
Approved |
no |
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Call Number |
Admin @ si @ AGL2012; ADAS @ adas @ agl2012a |
Serial |
2022 |
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Permanent link to this record |
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Author |
Ivo Everts; Jan van Gemert; Theo Gevers |
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Title |
Per-patch Descriptor Selection using Surface and Scene Properties |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7577 |
Issue |
VI |
Pages |
172-186 |
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Abstract |
Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool. |
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Address |
Florence, Italy |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33782-6 |
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ECCV |
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Notes |
ALTRES;ISE |
Approved |
no |
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Call Number |
Admin @ si @ EGG2012 |
Serial |
2023 |
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Permanent link to this record |
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Author |
Hamdi Dibeklioglu; Theo Gevers; Albert Ali Salah |
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Title |
Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7574 |
Issue |
III |
Pages |
525-538 |
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Abstract |
Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous/posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics. |
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Address |
Florence, Italy |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33711-6 |
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Conference |
ECCV |
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Notes |
ALTRES;ISE |
Approved |
no |
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Call Number |
Admin @ si @ DGS2012 |
Serial |
2024 |
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Permanent link to this record |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Non-Rigid Shape Registration: A Single Linear Least Squares Framework |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7578 |
Issue |
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Pages |
264-277 |
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Abstract |
This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided. |
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Address |
Florencia |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33785-7 |
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ECCV |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RoS2012a |
Serial |
2158 |
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Permanent link to this record |
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Author |
Patricia Marquez;Debora Gil;Aura Hernandez-Sabate |
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Title |
A Complete Confidence Framework for Optical Flow |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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Volume |
7584 |
Issue |
2 |
Pages |
124-133 |
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Keywords |
Optical flow, confidence measures, sparsification plots, error prediction plots |
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Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations. |
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Springer-Verlag |
Place of Publication |
Florence, Italy, October 7-13, 2012 |
Editor |
Andrea Fusiello, Vittorio Murino ,Rita Cucchiara |
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LNCS |
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ISBN |
978-3-642-33867-0 |
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ECCVW |
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Notes |
IAM;ADAS; |
Approved |
no |
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Call Number |
IAM @ iam @ MGH2012b |
Serial |
1991 |
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Permanent link to this record |
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Author |
David Masip; Alexander Todorov; Jordi Vitria |
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Title |
The Role of Facial Regions in Evaluating Social Dime |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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Volume |
7584 |
Issue |
II |
Pages |
210-219 |
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Keywords |
Workshops and Demonstrations |
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Abstract |
Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics. |
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Address |
Florence, Italy |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Andrea Fusiello, Vittorio Murino, Rita Cucchiara |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33867-0 |
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ECCVW |
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Notes |
OR;MV |
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no |
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Call Number |
Admin @ si @ MTV2012 |
Serial |
2171 |
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Permanent link to this record |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Pose-Invariant Face Recognition in Videos for Human-Machine Interaction |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
Abbreviated Journal |
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7584 |
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Pages |
566.575 |
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Abstract |
Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach. |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33867-0 |
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Conference |
ECCVW |
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Notes |
OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ RaD2012e |
Serial |
2182 |
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Author |
Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez |
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Title |
Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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Volume |
7584 |
Issue |
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Pages |
586-595 |
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Keywords |
road detection |
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Abstract |
Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo. |
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Publisher |
Springer Berlin Heidelberg |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33867-0 |
Medium |
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Area |
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Expedition |
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Conference |
ECCVW |
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Notes |
ADAS;ISE |
Approved |
no |
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Call Number |
Admin @ si @ ALG2012; ADAS @ adas |
Serial |
2187 |
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Permanent link to this record |