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Fahad Shahbaz Khan, Muhammad Anwer Rao, Joost van de Weijer, Andrew Bagdanov, Antonio Lopez and Michael Felsberg. 2013. Coloring Action Recognition in Still Images. IJCV, 105(3), 205–221.
Abstract: 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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Xavier Boix, Josep M. Gonfaus, Joost van de Weijer, Andrew Bagdanov, Joan Serrat and Jordi Gonzalez. 2012. Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation. IJCV, 96(1), 83–102.
Abstract: The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
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A. Pujol, Jordi Vitria, Felipe Lumbreras and Juan J. Villanueva. 2001. Topological principal component analysis for face encoding and recognition. PRL, 22(6-7), 769–776.
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Jaume Amores and Petia Radeva. 2005. Registration and Retrieval of Highly Elastic Bodies using Contextual Information. PRL, 26(11), 1720–1731.
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Jaume Amores and Petia Radeva. 2005. Retrieval of IVUS Images Using Contextual Information and Elastic Matching.
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Jaume Amores, N. Sebe and Petia Radeva. 2006. Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier. PRL, 27(3), 201–209.
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Jaume Amores, N. Sebe and Petia Radeva. 2007. Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms.
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva and Tian Qi. 2008. Distance Learning for Similarity Estimation.
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Daniel Ponsa, Robert Benavente, Felipe Lumbreras, J. Martinez and Xavier Roca. 2003. Quality control of safety belts by machine vision inspection for real-time production.
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Jose Manuel Alvarez, Theo Gevers and Antonio Lopez. 2010. Learning photometric invariance for object detection. IJCV, 90(1), 45–61.
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
Keywords: road detection
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