Object Categorization Using PLSA Based on Weighting

Hyun-Chul Song; In-Teck Whoang; Kwang-Nam Choi

Object Categorization Using PLSA Based on Weighting

Hyun-Chul Song, In-Teck Whoang, Kwang-Nam Choi, Journal of Internet Computing and Services, Vol. 10, No. 4, pp. 45-54, Aug. 2009
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Keywords: SIFT, PLSA, Object Categorization, Bag of Words

Abstract

In this paper we propose a new approach that recognizes the similar categories by weighting distinctive features. The approach is based on the PLSA that is one of the effective methods for the object categorization. PLSA is introduced from the information retrieval of text domain. PLSA, unsupervised method, shows impressive performance of category recognition. However, it shows relatively low performance for the similar categories which have the analog distribution of the features. In this paper, we consider the effective object categorization for the similar categories by weighting the mainly distinctive features. We present that the proposed algorithm, weighted PLSA, recognizes similar categories. Our method shows better results than the standard PLSA.

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Cite this article

[APA Style]

Song, H., Whoang, I., & Choi, K. (2009). Object Categorization Using PLSA Based on Weighting. Journal of Internet Computing and Services, 10(4), 45-54.

[IEEE Style]

H. Song, I. Whoang, K. Choi, "Object Categorization Using PLSA Based on Weighting," Journal of Internet Computing and Services, vol. 10, no. 4, pp. 45-54, 2009.

[ACM Style]

Hyun-Chul Song, In-Teck Whoang, and Kwang-Nam Choi. 2009. Object Categorization Using PLSA Based on Weighting. Journal of Internet Computing and Services, 10, 4, (2009), 45-54.