Inductive Transfer Deep Hashing for Image Retrieval

With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar codes. Supervised methods can handle such semantic similarity but they are prone to overfitting when the labeled data is few or noisy. In this paper, we concentrate on this issue and propose a novel Inductive Transfer Deep Hashing (ITDH) approach for semantic hashing based image retrieval. A transfer deep learning algorithm has been employed to learn the robust image representation, and the neighborhood-structure preserved method has been used to mapped the image into discriminative hash codes in hamming space. The combination of the two techniques ensures that we obtain a good feature representation and a fast query speed without depending on large amounts of labeled data. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art methods.