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Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga |
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Title |
Beyond Eleven Color Names for Image Understanding |
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2018 |
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Machine Vision and Applications |
Abbreviated Journal |
MVAP |
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29 |
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2 |
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361-373 |
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Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
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Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. |
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LAMP; NEUROBIT; 600.068; 600.109; 600.120 |
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Admin @ si @ YYW2018 |
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3087 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma |
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Title |
Scale coding bag of deep features for human attribute and action recognition |
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Journal Article |
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2018 |
Publication |
Machine Vision and Applications |
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MVAP |
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29 |
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1 |
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55-71 |
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Action recognition; Attribute recognition; Bag of deep features |
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Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. |
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LAMP; 600.068; 600.079; 600.106; 600.120 |
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Admin @ si @ KWR2018 |
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3107 |
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Mikhail Mozerov; Joost Van de Weijer |
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One-view occlusion detection for stereo matching with a fully connected CRF model |
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2019 |
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IEEE Transactions on Image Processing |
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TIP |
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28 |
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6 |
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2936-2947 |
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Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter |
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In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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LAMP; 600.098; 600.109; 602.133; 600.120 |
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Admin @ si @ MoW2019 |
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3221 |
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Author |
Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan |
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Title |
Synthetic Data Generation for End-to-End Thermal Infrared Tracking |
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Year |
2019 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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28 |
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4 |
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1837 - 1850 |
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The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers. |
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LAMP; 600.141; 600.120 |
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Admin @ si @ YGW2019 |
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3228 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz; Chengpeng Chen |
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Title |
Learning Effective RGB-D Representations for Scene Recognition |
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Journal Article |
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Year |
2019 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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28 |
Issue |
2 |
Pages |
980-993 |
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Deep convolutional networks can achieve impressive results on RGB scene recognition thanks to large data sets such as places. In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper. The first limitation is the lack of depth data for training deep learning models. Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a two-step training approach that directly learns effective depth-specific features using weak supervision via patches. The resulting RGB-D model also benefits from more complementary multimodal features. Another limitation is the short range of depth sensors (typically 0.5 m to 5.5 m), resulting in depth images not capturing distant objects in the scenes that RGB images can. We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes. Focusing on this scenario, we introduce the ISIA RGB-D video data set to evaluate RGB-D scene recognition with videos. Our video recognition architecture combines convolutional and recurrent neural networks that are trained in three steps with increasingly complex data to learn effective features (i.e., patches, frames, and sequences). Our approach obtains the state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition. |
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LAMP; 600.141; 600.120 |
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Admin @ si @ SJH2019 |
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3247 |
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