A deep neural network based hashing for efficient image retrieval

Learning valid similarities is a vital problem in hashing methods, especially in large-scale image search. Similarity pertained hashing method is widely used in image retrieval for its high quality compact binary code mapping. The hashing scheme of most existing hashing methods is that the input data is encoded as a vector of visual features and hashed into binary hash codes via projection functions or quantization methods afterward. However, this separated pipeline may prone to lose accurate similarities of images, since the limited compatible domain between visual feature vectors generation and binary codes mapping process. Encouraged by the extraordinary image representation learning ability of deep neural networks in classification, we propose a structure that merges binary code generation process within deep neural networks for efficient image retrieval. The proposed architecture contains two fundamental blocks. The stacked convolution layers of Network In Network with global average pooling compute effective image representation and the embedded latent layer with binary activation functions learn binary hash codes simultaneously. Experiments show that the proposed method gains improvement over several state-of-the-art hashing methods.