Object Classification Method Based on Weakly Supervised E2LSH and Saliency Map Weighting

ZHAO Yongwei; LI Bicheng; KE Shengcai

doi:10.11999/JEIT150337

Volume 38 Issue 1

Jan. 2016

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ZHAO Yongwei, LI Bicheng, KE Shengcai. Object Classification Method Based on Weakly Supervised E2LSH and Saliency Map Weighting[J]. Journal of Electronics & Information Technology, 2016, 38(1): 38-46. doi: 10.11999/JEIT150337

Citation:

ZHAO Yongwei, LI Bicheng, KE Shengcai. Object Classification Method Based on Weakly Supervised E2LSH and Saliency Map Weighting[J]. Journal of Electronics & Information Technology, 2016, 38(1): 38-46. doi: 10.11999/JEIT150337

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Object Classification Method Based on Weakly Supervised E2LSH and Saliency Map Weighting

doi: 10.11999/JEIT150337

Funds:

The National Natural Science Foundation of China (60872142, 61301232)

Received Date: 2015-03-23
Rev Recd Date: 2015-09-09
Publish Date: 2016-01-19

Abstract

Abstract

The most popular approach in object classification is based on the bag of visual-words model. However, there are several fundamental problems that restricts the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification method based on weakly supervised Exact Euclidean Locality Sensitive Hashing (E2LSH) and saliency map weighting is proposed. Firstly, E2LSH is employed to generate a group of visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, Graph-Based Visual Saliency (GBVS) algorithm is applied to detect the saliency map of different images and visual words are weighted according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results on datasets of Caltech-256 and Pascal 2007 indicate that the distinguishability of objects is effectively improved and the proposed method is superior to the state- of-the-art object classification methods.
- Object classification,
- Bag of visual words model,
- Exact Euclidean Locality Sensitive Hashing (E2LSH),
- Visual saliency map,
- Visual language model

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References(36)

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