Abstract
Relevance feedback plays as an important role in sketch retrieval as it does in existing content-based retrieval. This paper presents a method of relevance feedback for sketch retrieval by means of Linear Programming (LP) classification. A LP classifier is designed to do online training and feature selection simultaneously. Combined with feature selection, it can select a set of user-sensitive features and perform classification well facing a small number of training samples. Experiments prove the proposed method both effective and efficient for relevance feedback in sketch retrieval.
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Li, B., Sun, Z., Liang, S., Zhang, Y., Yuan, B. (2006). Relevance Feedback for Sketch Retrieval Based on Linear Programming Classification. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_24
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DOI: https://doi.org/10.1007/11922162_24
Publisher Name: Springer, Berlin, Heidelberg
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