Convolutional neural network based dictionary learning to create hash codes for content-based image retrieval

Abstract

Abstract This study investigates the suitability of sparse vectors in the dictionary learning (DL) method for content-based image retrieval (CBIR) tasks. Since DL usually performs the learning process in an unsupervised manner, it cannot generate robust features for the retrieval task, especially if a complex background is involved. In order to overcome this drawback, a DL approach using the feature representation power of the convolutional neural network (CNN) is proposed. The initialization values of the dictionaries in the proposed CNN based DL method are taken randomly from the middle layers of the CNN architecture. The vector of each image obtained from the CNN architecture is used as DL input. The lambda vectors produced by the DL structure are converted into binaries. In this way, DL acts as a hash code generator. The performance of the proposed framework is tested on modified COREL dataset. The results prove that it is an open-to-improvement approach and is promising.