Abstract
In Internet image retrieval, returned results may fail to satisfy the retrieval intentions of users because of noisy annotations. Solving the ambiguity in image retrieval by combining text features and visual information has been a challenging problem. In this paper, we propose a convenient and precise approach for Internet image retrieval called combined retrieval (CR), which costs minimized extra feedback to retrieve more results reflecting the query intentions of users. CR is used as a plug-in to commercial image search engines, such as Google and Bing, which are defined as host image search engines (HISE). First, in the returned result from HISE, document analysis is utilized to construct the image categories based on the Wikipedia categorical index. Returned images will be automatically categorized, and a convenient interface is provided for user feedback. Second, we describe the re-retrieval algorithm in which image data combined with particular text information will be sent to the HISE for re-retrieval. Finally, a perceptual hash based re-rank algorithm to optimize the returned images is proposed. Experimental results indicate that CR can significantly improve the retrieval performance with minimum effort and can provide a notably convenient user experience.
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