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Image Retrieval Based on Texture Semantic Description

Thusnavis Bella Mary.I, A. Vasuki


In a traditional Content Based Image Retrieval (CBIR) system, an image is represented by a set of low-level visual features, which are generally not effective and efficient in representing the image contents, and they also have no direct correlation with high-level semantic information. The gap between high-level information and low-level features is the fundamental difficulty that hinders the improvement of the image retrieval accuracy. This paper proposes a semantic based image retrieval system, with high level semantic learning to reduce the problem of ‘semantic gap’. In this work texture features are extracted using GLCM and corresponding semantic interpretation are given to each feature. Images are retrieved according to user satisfaction and thereby reducing the semantic gap by using texture features and linguistic term.


CBIR, Semantic Gap, low level features, High Level Semantics, Linguistic terms.

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