Abstract
In recent years, content-based image retrieval achieved continuous development, the main goal so far has been to retrieve similar objects for a given query, and only the relevance is cared in retrieval system, so many duplicate or near duplicate documents retrieved in response to a query. For efficient content-based image retrieval, we propose the Content-based Diversifying Leaf Image Retrieval in this paper. In order to make the retrieval results have relevance and diversity, we extract leaf image feature and use the relevance feedback technique based of SVM and the AP clustering algorithm. We also proposed a new evaluation function - Maximal Scatter Diversity (MSD) static evaluation function. Experimental results show that our approach can achieve good performance with improving the diversity of the retrieval results without reduction of their relevance.
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Zhu, SP., Du, JX., Zhai, CM. (2013). Content-Based Diversifying Leaf Image Retrieval. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_5
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DOI: https://doi.org/10.1007/978-3-642-39482-9_5
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