-This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level cooccurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.
In this paper we implemented feature selection for content based image retrieval using evolutionary computation. In this system, we used feature extraction techniques for color, texture and shape. The three techniques are used for feature extraction such as color moment, Gabor filter, and Edge histogram descriptor. To reduce the dimensionality and find best optimal features from feature set using feature selection based on two evolutionary computations i.e. Genetic algorithm, and Binary Bat Algorithm. These subset features are divided into similar image classes using k-means clustering algorithm for fast retrieval and improve the computational time. We compared these two algorithms with different parameters i.e. precision, recall and computational time of image retrieval. The experimental result shows feature selection using BBA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to feature selection using GA. In this method selects different combinations of features which user retrieves more similar images. The CBIR system is more efficient and better performs using feature selection based on BBA.
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