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Moreover, CMVF is not only constrained to visual properties, but can also incorporate human classification criteria to further strengthen image retrieval process. The controlled study present in this paper concentrates on CMVF's performance on images, examining how the incorporation of extra features into the indexing affects both efficiency and effectiveness. Analysis and empirical evidence suggest that the inclusion of extra visual features can significantly improve system performance. Furthermore, it demonstrated that CMVF's effectiveness is robust against various kinds of common image distortions and initial (random) configuration of neural network.<\/jats:p>","DOI":"10.1142\/s0219467807002751","type":"journal-article","created":{"date-parts":[[2007,7,20]],"date-time":"2007-07-20T01:01:52Z","timestamp":1184893312000},"page":"551-581","source":"Crossref","is-referenced-by-count":1,"title":["AN EMPIRICAL STUDY OF QUERY EFFECTIVENESS IMPROVEMENT VIA MULTIPLE VISUAL FEATURE INTEGRATION"],"prefix":"10.1142","volume":"07","author":[{"given":"JIALIE","family":"SHEN","sequence":"first","affiliation":[{"name":"School of Information Systems, Singapore Management University, 80 Stanford Road, Singapore 178902, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JOHN","family":"SHEPHERD","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ANNE H. 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