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Narrowing Semantic Gap in Content-based Image Retrieval

Naveen kumar Joshi, Ritesh Khedekar

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Abstract

Due to the low-level image features it utilizes, the semantic gap problem is hard to bridge and performance of CBIR systems is still far away from users’ expectation. Image annotation, region-based image retrieval and relevance feedback are three main approaches for narrowing the “semantic gap”. In this paper, recent development in these fields is reviewed and some future directions are proposed in the end.

About this research paper

What this paper is about

Due to the low-level image features it utilizes, the semantic gap problem is hard to bridge and performance of CBIR systems is still far away from users’ expectation. Image annotation, region-based image retrieval and relevance feedback are three main approaches for narrowing the “semantic gap”. In this paper, recent development in these fields is reviewed and some future directions are proposed in the end.

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Method / approach

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Available abstract

Due to the low-level image features it utilizes, the semantic gap problem is hard to bridge and performance of CBIR systems is still far away from users’ expectation. Image annotation, region-based image retrieval and relevance feedback are three main approaches for narrowing the “semantic gap”. In this paper, recent development in these fields is reviewed and some future directions are proposed in the end.

Key concepts: Semantic gap, Image retrieval, Computer science, Automatic image annotation, Information retrieval, Image (mathematics), Bridge (graph theory), Content-based image retrieval

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