A content-based image retrieval system is proposed using an optimized weighted feature voting technique with multifeatures and multidistance measures. The proposed system consists of two phases: enrollment phase and querying phase. In enrollment phase, six features have been extracted from each enrollment image. These features are color histogram, edge histogram, edge direction histogram, hierarchical annular histogram, Gabor filter, and co-occurrence matrix. The extracted features are weighted, and feature weights are optimized using the four optimization techniques. Particle swarm optimization, ant lion optimizer, bird swarm algorithm, and whale optimization algorithm are applied and compared to decide the suitable technique. In querying phase, the same features are extracted from the query image and comparing these features to the extracted features from the database images through the matching measures. Three distance measures have been used and compared for the matching process: histogram intersection, Euclidean distance, and cosine distance. Based on these distances and the feature weights, the class of the query image is identified using the weighted feature voting mechanism. Finally, the related images are retrieved based on the feature that returned the maximum number of images in the identified class. For the validation and performance evaluations of the proposed system; Wang, Caltech101, and UW datasets are used. The experimental results show that the proposed method achieves improved precision in comparison with existing methods with 93% for Wang database, 92% for UW database, and 94% for Caltech101 database.
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