Rapid image retrieval with binary hash codes based on deep learning

With the ever-growing large-scale image data on the web, rapid image retrieval has become one of the hot spots in the multimedia field. And it is still very difficult to reliable image retrieval due to the complex image appearance variations. Inspired by the robustness of convolutional neural networks features, we propose an effective deep learning framework to generate compact similarity-preserving binary hash codes for rapid image retrieval. Our main idea is incorporating deep convolutional neural network (CNN) into hash functions to jointly learn feature representations and mappings from them to hash codes. In particular, our approach which learns hash codes and image representations takes pairs of images as training inputs. Meanwhile, an effective loss function is used to maximize the differentiability of the output space by encoding the supervised information from the input image pairs. We extensively evaluate the retrieval performance on two large-scale datasets CIFAR-10 and NUS-WIDE, and the evaluation shows that our method gives a better performance than traditional hashing learning methods in image retrieval.