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Content-Based Image Retrieval (CBIR) is also known as Query by Image Content (QBIC) that presents the technologies allowing to organize digital pictures by their visual features. They are based on the application of computer vision techniques to the image retrieval problem in large databases. CBIR consists of retrieving the most visually similar images to a given query image from a database of images. Retrieval of images is based not on keywords or annotations but on features extracted directly from the image data. Therefore, in this research study, the final output is retrieved from the database using feature extraction from the input query images.In this research study, Haralick Features are extracted, which is also known as Gray-Level Co-Occurrence Matrix (GLCM) along with Local Binary Pattern (LBP), and histogram of oriented gradients (HOG) features. When more number of features are used, it will increase the complexity of classifier to achieve better results. Therefore, feature selection technique called Pigeon Inspired based Optimization (PIO) is used in this research study. In the testing process, when the user gives a query images, the process will be same as training process and finally, to extract the relevant images, the training images are taken from the dataset and compared with the query images using Artificial Neural Network (ANN) classifier. The experiments are carried out using publicly available dataset called WANG and compared with existing techniques in terms of accuracy, precision, recall and F-measure.