Image Classification Approach for Improving CBIR System Performance 


Vol. 41,  No. 7, pp. 816-822, Jul.  2016


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  Abstract

Content-Based image retrieval is a method to search by image features such as local color, texture, and other image content information, which is different from conventional tag or labeled text-based searching. In real life data, the number of images having tags or labels is relatively small, so it is hard to search the relevant images with text-based approach. Existing image search method only based on image feature similarity has limited performance and does not ensure that the results are what the user expected. In this study, we propose and validate a machine learning based approach to improve the performance of the image search engine. We note that when users search relevant images with a query image, they would expect the retrieved images belong to the same category as that of the query. Image classification method is combined with the traditional image feature similarity method. The proposed method is extensively validated on a public PASCAL VOC dataset consisting of 11,530 images from 20 categories.

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  Cite this article

[IEEE Style]

W. Han and K. Sohn, "Image Classification Approach for Improving CBIR System Performance," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 816-822, 2016. DOI: .

[ACM Style]

Woo-Jin Han and Kyung-Ah Sohn. 2016. Image Classification Approach for Improving CBIR System Performance. The Journal of Korean Institute of Communications and Information Sciences, 41, 7, (2016), 816-822. DOI: .

[KICS Style]

Woo-Jin Han and Kyung-Ah Sohn, "Image Classification Approach for Improving CBIR System Performance," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 816-822, 7. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880