Abstract
Semantic-based image retrieval is the desired target of Content-based image retrieval (CBIR). In this paper, we proposed a new method to extract semantic information for CBIR using the relevance feedback results. Firstly it is assumed that positive and negative examples in relevant feedback are containing semantic content added by users. Then image internal semantic model (IISM) is proposed to represent comprehensive pair-wise correlation information for images through analyzing the feedback results. Finally, correlation learning method is proposed to represent the images’ pair-wise relationship based on statistical value of access path, access frequency, similarity factor and correlation factor. Experimental results on Corel datasets show the effectiveness of the proposed model and method.
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Duan, L., Mao, G., Gao, W. (2004). Correlation Learning Method Based on Image Internal Semantic Model for CBIR. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30542-2_22
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DOI: https://doi.org/10.1007/978-3-540-30542-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23977-2
Online ISBN: 978-3-540-30542-2
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