NDLI: Reducing the complexity of k-nearest diverse neighbor queries in medical image datasets through fractal analysis

Content Provider	IEEE Xplore Digital Library
Author	Dias, R.L. Bueno, R. Ribeiro, M.X.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput., Fed. Univ. of Sao Carlos, Sao Carlos, Brazil (Dias, R.L.; Bueno, R.; Ribeiro, M.X.)
Abstract	Content-Based Image Retrieval (CBIR) Systems allow the search of images by similarity employing a numeric representation automatically or semi-automatically obtained from them to perform the search. Nevertheless, the query result does not always bring what the user expected. In this sense, CBIR systems face the semantic gap problem. One way of overcoming this problem is by the addition of diversity in query execution, so that the user can ask the system to return the most varied images regarding some similarity criteria. However, applying diversity on large datasets has a prohibitive computational cost and, moreover, the result often differs from the expected with a resulting subset that has images with high dissimilarity to the query image. In this paper we propose an approach to reduce the computational cost of Content-Based Image Retrieval systems regarding similarity and diversity criteria. The proposed approach employs dataset fractals analysis to estimate a suitable radius for a database subset to perform a similarity query regarding diversity. It selects closer images to the query center and applies the diversity factor to the subset, providing not only a better comprehension of the impact of the diversity factor to the query result, but also an improvement in execution time.
Starting Page	101
Ending Page	106
File Size	1173477
Page Count	6
File Format	PDF
ISBN	9781479910533
DOI	10.1109/CBMS.2013.6627772
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-20
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fractals Visualization Image retrieval Medical diagnostic imaging Vectors Hardware medical image retrieval similarity queries diversity fractal analysis
Content Type	Text
Resource Type	Article

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