Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 27, Issue 8, Pages 1691-1699Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2016.2527380
Keywords
Computer vision; image processing; pattern classification
Categories
Funding
- National Natural Science Foundation of China [61303154, 61332016]
- National Basic Research Program of China (973 Program) [2012CB316400, 2015CB351802]
- Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences
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The use of local features for image representation has become popular in recent years. Local features are often used in the bag-of-visual-words scheme. Although proven effective, this method still has two drawbacks. First, local regions from which local features are extracted are not discriminative enough for visual tasks. Hence, the combination of local features is necessary. Second, the semantic gap between visual features and human perception also hinders the performance. To address these two problems, in this paper, we propose a novel contextual exemplar classifier-based method for image representation and apply it for classification tasks. Each exemplar classifier is trained to separate one training image from the other images of different classes. We partition each image into a number of regions and use the responses of these exemplar classifiers as the image region's representation. The contextual relationship is then modeled using mixture Dirichlet distributions. A bilayer model is used to predict image classes with L-2 constraints. Experimental results on the Natural Scene, Caltech-101/256, Flower-17/102, and SUN-397 data sets show that the proposed method is able to outperform the state-of-the-art local feature-based methods for image classification.
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