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Author Xinhang Song; Shuqiang Jiang; Luis Herranz edit  doi
openurl 
  Title Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold Type Journal Article
  Year 2017 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 26 Issue 6 Pages (down) 2721-2735  
  Keywords  
  Abstract Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy.  
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  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ SJH2017a Serial 2963  
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 6 Pages (down) 2193-2204  
  Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models  
  Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.  
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  Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no  
  Call Number Admin @ si @ CPR2014b Serial 2470  
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Author Xialei Liu; Joost Van de Weijer; Andrew Bagdanov edit   pdf
url  doi
openurl 
  Title Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank Type Journal Article
  Year 2019 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 41 Issue 8 Pages (down) 1862-1878  
  Keywords Task analysis;Training;Image quality;Visualization;Uncertainty;Labeling;Neural networks;Learning from rankings;image quality assessment;crowd counting;active learning  
  Abstract For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abundantly available. In this paper, we show how ranking can be used as a proxy task for some regression problems. As another contribution, we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch network architecture. We apply our framework to two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning and we show that this reduces labeling effort by up to 50 percent.  
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  Notes LAMP; 600.109; 600.106; 600.120 Approved no  
  Call Number LWB2019 Serial 3267  
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Author Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan edit   pdf
doi  openurl
  Title Synthetic Data Generation for End-to-End Thermal Infrared Tracking Type Journal Article
  Year 2019 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 28 Issue 4 Pages (down) 1837 - 1850  
  Keywords  
  Abstract 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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  Notes LAMP; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ YGW2019 Serial 3228  
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Author Carlo Gatta; Francesco Ciompi edit   pdf
doi  openurl
  Title Stacked Sequential Scale-Space Taylor Context Type Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 36 Issue 8 Pages (down) 1694-1700  
  Keywords  
  Abstract We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.  
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  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; MILAB; 601.160; 600.079 Approved no  
  Call Number Admin @ si @ GaC2014 Serial 2466  
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