Fast and Efficient Image Retrieval via FullyConvolutional Hashing Network With the rapid development of information technology, contentbased image retrieval and related technologies have become increasingly important. The hash method can represent an image with a sequence of binary codes consisting of 0 and 1, while the application of a convolutional neural networks can learn directly from the image to a discriminative binary code. We propose a deep hash algorithm based on full convolutional neural network, which can reduce the number of network parameters and have the retrieval performance not lower than the cuttingedge method in this field. The proposed network structure uses convolutional layers and the global average pooling layer to replace fullyconnected layers in current deep hashing network structures, which significantly reduces the complexity of the network and improved its training performance. This method is called Fullyconvolutional hashing networks (FCHN), and experiments were carried out on several publicized datasets to verify the effectiveness of the method.

Fast Semantic Preserving Hashing for LargeScale CrossModal Retrieval Most Crossmodal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving timeconsuming training procedures for largescale dataset. To tackle these issues, we first formulate the learning of similaritypreserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to largescale crossmodal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modalityspecific projection function learning and an efficient closedform solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the stateofthearts.