An Image Retrieval Method for Binary Images Based on DBN and Softmax Classifier

Currently, the common methods for image retrieval are content-based, while the abilities of image feature representation of these methods are very limited. In this paper, a new image retrieval method for binary images based on Deep Belief Networks (DBN) and Softmax classifier is proposed, which classifies the image data-set into some categories with the DBN and Softmax classifier first, and then classifies the query image in the same way, and those images in the same category will be returned as the similar images of the query image. Unlike the existing image retrieval models, the new method aims to provide a more effective representation and extraction measure by simulating the architecture of human visual system, and it is not necessary to set the threshold manually for this method like most of the existing methods based on the hamming distance computation. Experimental results show that the proposed method can get better recall and precision than some existing methods, such as perceptual hash algorithm and shape-based algorithm.