文章基本信息

标题：Content-based Image Retrieval by Information Theoretic Measure
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作者：Madasu Hanmandlu ; Anirban Das
期刊名称：Defence Science Journal
印刷版ISSN：0976-464X
出版年度：2011
卷号：61
期号：5
页码：415-430
DOI：10.14429/dsj.61.1177
语种：English
出版社：Defence Scientific Information & Documentation Centre
摘要：Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset. Defence Science Journal, 2011, 61(5), pp.415-430 , DOI:http://dx.doi.org/10.14429/dsj.61.1177
关键词：Image retrieval;fuzzy features;descriptors;entropy;indexing