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An enhanced texture-based image retrieval approach with features selected from integration of feature extraction techniques

Texture is vital in characterising images for effective content-based image retrieval. Integrating features from various feature extraction techniques improves the performance of decision system in comparison to individual techniques as it provides complimentary information as a whole. However, this integration creates a large feature vector which may contain irrelevant and redundant features and hence degrade the performance. Therefore, we propose a three-phase texture-based image retrieval approach for enhanced performance. In the first phase, pool of texture features from seven feature extraction techniques is created. In the second phase, some popular feature selection techniques are applied to this pool to obtain a reduced set of relevant and non-redundant features. In the third phase, three well-known distance measures are utilised to retrieve images based on the reduced features set. The performance of the proposed approach is evaluated on Brodatz dataset. The proposed approach outperforms individual feature extraction techniques.