Özet:

Abstract Content-Based Image retrieval(CBIR) is a technique to search and retrieve similar images from large multimedia databases and an IR system is regarded as efficient if it can retrieve all the images to meet the user’s needs. There are many advanced machine-learning technologies such as deep neural networks(DNN), convolutional neural networks(CNN), and transfer-learning(TL), which are gaining greater importance in image-related tasks. In this paper an efficient framework for content-based image retrieval system adapting transfer-learning on pre-trained CNNs (ResNet18, GoogLeNet, AlexNet) using query-by-image method is proposed, the method explores classification-score descriptors for IR and employ distance metrics for similarity matching. The framework prescribes transfer-learning for efficient retraining of pre-trained CNNs on small datasets chosen from the Wang database. Thirty-plus experiments are designed for finding optimal values of the hyper-parameters and exploring the suitability of six popular distance metrics namely Euclidean, seuclidean, Cityblock, Cosine, Mahalanobis, and Chebychev. After extensive experimentation, a new efficient framework for CBIR using CNN classification scores is proposed and the new framework of CBIR achieves the image retrieval accuracy of 99.45% on natural scene images of 20 classes of the Wang dataset. The experimentations show that the proposed framework is efficient for content-based image retrieval system.

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