Content Based Image Retrieval (CBIR) is a technique, which is utilized for retrieving identical images from an image database. Dimensionality reduction of the feature space has a significant role in improving the classifiers’ performance. For concerns involving storage and retrieval efficacy, dimensionality reduction in CBIR systems is essential. This research work introduces an efficient and new approach for improving the performance of CBIR based on Scale Invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. After this, bat algorithm is presented for dimensionality reduction, which considerably increases the classification accuracy. This paper provides the comparison of the classification efficacy of classifiers including Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) for CBIR. The experimental outcomes of the newly introduced classifiers are compared prior and after dimensionality reduction. The evaluation is performed on various image databases for showing the reliability of the newly introduced approach in terms of Precision, Recall, and Accuracy.

CBIR, Dimensionality Reduction, SIFT, Bat Algorithm, SVM, CART, RF, and Image Retrieval

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