Supervised Hashing Method Research Articles

Supervised deep hashing for scalable face image retrieval

Hashing has been widely utilized for Approximate Nearest Neighbor (ANN) search due to its fast retrieval speed and low storage cost. In this work, we propose a novel supervised hashing method for scalable face image retrieval, i.e., Deep Hashing based on Classification and Quantization errors (DHCQ), by simultaneously learning feature representations of images, hash codes and classifiers. The supervised information and the deep architecture are collaboratively explored. Specifically, a deep convolutional network is introduced to learn discriminative feature representations, which are directly used to generate hash codes and predict labels of images. The quantization errors and the prediction errors jointly guide the learning of the deep network. They are highly interrelated and promoted each other. It is worth noting that the proposed method is a general hashing method and can be applied to the general image retrieval task. Extensive experiments on two face image datasets and one general image dataset demonstrate the effectiveness of the proposed method compared with several state-of-the-art hashing methods.

Supervised Coarse-to-Fine Semantic Hashing for cross-media retrieval

Due to its storage efficiency and fast query speed, cross-media hashing methods have attracted much attention for retrieving semantically similar data over heterogeneous datasets. Supervised hashing methods, which utilize the labeled information to promote the quality of hashing functions, achieve promising performance. However, the existing supervised methods generally focus on utilizing coarse semantic information between samples (e.g. similar or dissimilar), and ignore fine semantic information between samples which may degrade the quality of hashing functions. Accordingly, in this paper, we propose a supervised hashing method for cross-media retrieval which utilizes the coarse-to-fine semantic similarity to learn a sharing space. The inter-category and intra-category semantic similarity are effectively preserved in the sharing space. Then an iterative descent scheme is proposed to achieve an optimal relaxed solution, and hashing codes can be generated by quantizing the relaxed solution. At last, to further improve the discrimination of hashing codes, an orthogonal rotation matrix is learned by minimizing the quantization loss while preserving the optimality of the relaxed solution. Extensive experiments on widely used Wiki and NUS-WIDE datasets demonstrate that the proposed method outperforms the existing methods.

Supervised Hashing for Image Retrieval via Image Representation Learning

Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of hand-crafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions. The proposed method has two stages. In the first stage, given the pairwise similarity matrix $S$ over training images, we propose a scalable coordinate descent method to decompose $S$ into a product of $HH^T$ where $H$ is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in $H$ and optionally the discrete class labels of the images. Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.