Example-based visual object counting with a sparsity constraint | IEEE Conference Publication | IEEE Xplore

Example-based visual object counting with a sparsity constraint

Publisher: IEEE

Abstract:

For existing mainstream visual object counting (VOC) methods, training data insufficiency will lead to significant performance degradation. To address this challenge, we ...View more

Abstract:

For existing mainstream visual object counting (VOC) methods, training data insufficiency will lead to significant performance degradation. To address this challenge, we propose a novel sparsity-constrained example-based VOC method. Given a test image, its counts are estimated by integrating over its density map, and our method will predict such density map based on patch using training examples. Specifically, image patches and their counterpart density maps generated from annotated training images share similar local geometry on manifolds. Such local geometry can be captured by locally linear embedding (LLE) only when data are well-sampled. However, training data are poorly sampled due to their insufficiency. To handle this problem, we impose sparsity on the local optimization based on LLE, where the chosen examples favor the similar structure of input patches. Extensive experiments on public datasets demonstrate the effectiveness and competitiveness of our method by using simple features and a few training images.
Date of Conference: 11-15 July 2016
Date Added to IEEE Xplore: 29 August 2016
ISBN Information:
Electronic ISSN: 1945-788X
Publisher: IEEE
Conference Location: Seattle, WA, USA

References

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