Abstract
Over many years, almost all research work in the content-based image retrieval (CBIR) has used Minkowski metric (or L p -norm) to measure similarity between images. However, those functions cannot adequately capture the nonlinear relationships in contextual information given by image datasets. In this paper, we present a new similarity measure reflecting the nonlinearity of contextual information. Moreover, we propose a new similarity ranking algorithm based on this similarity measure for effective CBIR. Our algorithm yields superior experimental results on real image database and demonstrates its effectiveness.
This work was supported by grant No. B1220-0501-0233 from the University Fundamental Research Program of the Ministry of Information & Communication in Republic of Korea.
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Cha, GH. (2006). Context-Sensitive Ranking for Effective Image Retrieval. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_34
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DOI: https://doi.org/10.1007/978-3-540-69423-6_34
Publisher Name: Springer, Berlin, Heidelberg
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