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- W2182798690 abstract "Typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how Histogram set theory can be effectively used for this purpose and describe an image retrieval system called HIRST (Histogram image retrieval system) which incorporates many of these ideas. HIRST can handle exemplar-based, graphicalsketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. HIRST uses Histogram attributed relational graphs (HARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-comp lexity Histogram graph matching algorith m is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorith m avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures." @default.
- W2182798690 created "2016-06-24" @default.
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- W2182798690 date "2013-01-01" @default.
- W2182798690 modified "2023-09-24" @default.
- W2182798690 title "An Histogram Based Approach for Content Based Image Mining" @default.
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