Image retrieval based on indexing and relevance feedback

• Published in: Pattern recognition letters Vol. 28; no. 3; pp. 357 - 366
• Main Authors: Saha, Sanjoy K., Das, Amit K., Chanda, Bhabatosh
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2007; Elsevier
• Subjects: Applied sciences; CBIR; Exact sciences and technology; Human perception based similarity measure; Image processing; Information theory; Information, signal and communications theory; Mann–Whitney test; Multidimensional indexing; Relevance feedback; Signal processing; Similarity measure; Telecommunications and information theory; Relevance feedback; Human perception based similarity measure; Multidimensional indexing; Similarity measure; Mann–Whitney test; CBIR; Performance evaluation; Similarity; Image retrieval; Search system; Search engine; High precision; Mann-Whitney test; Updating; Information retrieval; Entropy; Human perception based similarity measure: Multidimensional indexing; Subjective evaluation; Implementation; Content-based retrieval; Distance measurement; Indexing
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2006.04.005

In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure. It should be fast enough and must have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. The indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann–Whitney test. The test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. Then emphasis of the discriminating features are updated to improve the retrieval performance. The relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). The experiment justifies the effectiveness of the proposed methodologies. Finally, the indexing scheme and relevance feedback mechanism are combined to build up the search engine.