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Author |
Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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Title |
Saliency Estimation Using a Non-Parametric Low-Level Vision Model |
Type |
Conference Article |
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Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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433-440 |
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Keywords |
Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms |
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Abstract |
Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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no |
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Admin @ si @ MVO2011 |
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1757 |
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Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez |
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Title |
A Coarse-to-fine Approach for fast Deformable Object Detection |
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Conference Article |
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Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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1353-1360 |
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Colorado Springs; USA |
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Admin @ si @ PVG2011 |
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1764 |
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Author |
Miguel Oliveira; Angel Sappa; V.Santos |
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Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
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Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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201-208 |
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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ADAS |
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no |
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Admin @ si @ OSS2011; ADAS @ adas @ |
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1766 |
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Author |
Albert Gordo; Florent Perronnin |
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Title |
Asymmetric Distances for Binary Embeddings |
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Conference Article |
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Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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729 - 736 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
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Providence, RI |
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978-1-4577-0394-2 |
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DAG |
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no |
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Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
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1817 |
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Author |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
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Title |
Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
726-732 |
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We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
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Portland; Oregon; June 2013 |
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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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MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ HZM2012b |
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2046 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez |
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Title |
Color Attributes for Object Detection |
Type |
Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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3306-3313 |
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pedestrian detection |
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Abstract |
State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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Providence; Rhode Island; USA; |
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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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ADAS; CIC; |
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no |
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Call Number |
Admin @ si @ KRW2012 |
Serial |
1935 |
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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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2408-2415 |
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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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CIC |
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no |
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Call Number |
Admin @ si @ MMP2012a |
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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 |
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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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CIC |
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no |
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Call Number |
Admin @ si @ SPB2012 |
Serial |
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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2602-2609 |
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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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ISE |
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no |
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Admin @ si @ HGD2012 |
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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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2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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749-756 |
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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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1063-6919 |
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978-1-4673-1226-4 |
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ADAS |
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no |
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Admin @ si @ RSL2012b; ADAS @ adas @ |
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2033 |
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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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2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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3045-3052 |
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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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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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DAG |
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no |
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Call Number |
Admin @ si @ GRP2012 |
Serial |
2050 |
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Author |
Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat |
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Title |
Discriminative Color Descriptors |
Type |
Conference Article |
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Year |
2013 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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2866 - 2873 |
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Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200. |
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Portland; Oregon; June 2013 |
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1063-6919 |
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CIC; 600.048 |
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Admin @ si @ KWK2013a |
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2262 |
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Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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The Photometry of Intrinsic Images |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
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2506 |
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M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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Adaptive color attributes for real-time visual tracking |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1090 - 1097 |
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Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second. |
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Nottingham; UK; September 2014 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ DKF2014 |
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2509 |
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German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
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Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
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2016 |
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29th IEEE Conference on Computer Vision and Pattern Recognition |
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3234-3243 |
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Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
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Las Vegas; USA; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ RSM2016 |
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2739 |
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