Content-Based Image Retrieval Using Convolutional Neural Networks

Content-based image retrieval (CBIR) is a widely used technique for retrieval images from huge and unlabeled image databases. However, users are not satisfied with the traditional information retrieval techniques. Moreover, the emergence of web development and transmission networks and also the amount of images which are available to users continue to grow. Therefore, a permanent and considerable digital image production in many areas takes place. Hence, the rapid access to these huge collections of images and retrieve similar image of a given image (Query) from this large collection of images presents major challenges and requires efficient techniques. The performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurement. For this reason, we present, on this paper, a simple but effective deep learning framework based on Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for fast image retrieval composed of feature extraction and classification. From several extensive of empirical studies for a variety of CBIR tasks using image database, we obtain some encouraging results which reveals several important insights for improving the CBIR performance.