• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Object Classification based on Weakly Supervised E2LSH and Saliency map Weighting

Vol. 10, No. 1, January 30, 2016
10.3837/tiis.2016.01.021, Download Paper (Free):

Abstract

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting 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 based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized 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 weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.


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Cite this article

[IEEE Style]
Y. Zhao, B. Li, X. Liu and S. Ke, "Object Classification based on Weakly Supervised E2LSH and Saliency map Weighting," KSII Transactions on Internet and Information Systems, vol. 10, no. 1, pp. 364-380, 2016. DOI: 10.3837/tiis.2016.01.021.

[ACM Style]
Yongwei Zhao, Bicheng Li, Xin Liu, and Shengcai Ke. 2016. Object Classification based on Weakly Supervised E2LSH and Saliency map Weighting. KSII Transactions on Internet and Information Systems, 10, 1, (2016), 364-380. DOI: 10.3837/tiis.2016.01.021.