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Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |


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Title |
Semantic Pyramids for Gender and Action Recognition |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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23 |
Issue |
8 |
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3633-3645 |
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Abstract |
Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
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1057-7149 |
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CIC; LAMP; 601.160; 600.074; 600.079;MILAB;ADAS |
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Admin @ si @ KWR2014 |
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2507 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg |


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Title |
Coloring Action Recognition in Still Images |
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Journal Article |
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Year |
2013 |
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International Journal of Computer Vision |
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IJCV |
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105 |
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3 |
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205-221 |
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In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. |
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Springer US |
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0920-5691 |
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CIC; ADAS; 600.057; 600.048 |
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Admin @ si @ KRW2013 |
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2285 |
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A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |

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Topological principal component analysis for face encoding and recognition |
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2001 |
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Pattern Recognition Letters |
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PRL |
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22 |
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6-7 |
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769–776 |
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IF: 0.552 |
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ADAS;OR;MV |
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ADAS @ adas @ PVL2001 |
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155 |
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Author |
Jaume Amores; Petia Radeva |


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Title |
Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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2005 |
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Pattern Recognition Letters |
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PRL |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005b |
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592 |
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Author |
Jaume Amores; Petia Radeva |

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Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005a |
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593 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |

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Title |
Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier |
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Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
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PRL |
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27 |
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3 |
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201–209 |
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ADAS;MILAB |
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ADAS @ adas @ ASR2006 |
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643 |
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Jaume Amores; N. Sebe; Petia Radeva |

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Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms |
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2007 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) |
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ADAS;MILAB |
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ADAS @ adas @ ASR2007b |
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922 |
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Author |
Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi |

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Title |
Distance Learning for Similarity Estimation |
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2008 |
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IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462 |
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ADAS;MILAB |
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ADAS @ adas @ JAS2008 |
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961 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca |

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Title |
Quality control of safety belts by machine vision inspection for real-time production |
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Journal Article |
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2003 |
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Optical Engineering (IF: 0.877) |
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42 |
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4 |
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1114-1120 |
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SPIE |
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ADAS;ISE;CIC |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |


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Title |
Learning photometric invariance for object detection |
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Journal Article |
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2010 |
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International Journal of Computer Vision |
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IJCV |
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90 |
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1 |
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45-61 |
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road detection |
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Abstract |
Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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Springer US |
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0920-5691 |
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ADAS;ISE |
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ADAS @ adas @ AGL2010c |
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1451 |
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