An image retrieval technique based on texture features using semantic properties

Image retrieval is one of the most interesting and fastest growing research areas in all fields. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of low-level visual features; hence a dire...

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Bibliographic Details
Main Authors: Jisha, K.P, Thusnavis, Bella Mary. I, A., Vasuki
Format: Conference Proceeding
Language:English
Subjects:
CBIR
Complexity theory
Correlation
GLCM
Indexes
Navigation
Pragmatics
Semantic gap
Semantic terms
Semantics
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Summary:Image retrieval is one of the most interesting and fastest growing research areas in all fields. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of low-level visual features; hence a direct correlation with high-level semantic information will be absent. Therefore, a gap exists between high-level information and low-level features, which is the main reason that hinders the improvement of the image retrieval accuracy. In this work, main focus is on the semantic based image retrieval system using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. Based on the texture features, semantic interpretations are given to the extracted textures. The images are retrieved according to user satisfaction and thereby reduce the semantic gap between low level features and high level features.
DOI:10.1109/ICSIPR.2013.6497932