Recently, hashing has attracted much attention in visual information retrieval due to its low storage cost and fast query speed. The goal of hashing is to map original high-dimensional data into a low-dimensional binary-code space where the similar data points are assigned similar hash codes and dissimilar points are far away from each other. Existing unsupervised hashing methods mainly focus on recovering the pairwise similarity of the original data in hash space, but do not take specific measures to make the generated binary codes to be discriminative. To address this problem, this paper proposes a novel unsupervised hashing method, named “Discriminative Unsupervised Graph Hashing” (DUGH), which takes both similarity and dissimilarity of original data into consideration to learn discriminative binary codes. In particular, a probabilistic model is utilized to learn the encoding of original data in low-dimensional space, which models the original neighbor structure through both positive and negative edges in the KNN graph and then maximizes the likelihood of observing these edges. To efficiently and accurately measure the neighbor structure for largescale datasets, we propose an effective KNN graph construction algorithm based on the random projection tree and neighbor exploring techniques. The experimental results on one synthetic dataset and four typical real-world image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art unsupervised hashing methods.

Semi-Supervised Learning (SSL) aims to leverage unlabeled data to improve performance. Due to the lack of supervised information, previous works mainly focus on how to utilize the available unlabeled data to improve the training quality. However, the estimation of the data distribution revealed by the unlabeled examples might not be accurate as their classes are unknown. Inspired by the framework of Learning Using Privileged Information (LUPI), we propose to introduce an intelligent “teacher” which can utilize the privileged information (i.e. more precise explanations of training set) to improve the performance of SSL. This developed method is named as Privileged Semi-Supervised Learning (PSSL). Moreover, our method can be efficiently solved with l2-loss. Experimentally, we compare our PSSL with several typical algorithms on three real-world datasets, and the results suggest that our approach is able to achieve the state-of-the-art performance.

Recently, hashing has attracted considerable attention for nearest neighbor search due to its fast query speed and low storage cost. However, existing unsupervised hashing algorithms have two problems in common. Firstly, the widely utilized anchor graph construction algorithm has inherent limitations in local weight estimation. Secondly, the locally linear structure in the original feature space is seldom taken into account for binary encoding. Therefore, in this paper, we propose a novel unsupervised hashing method, dubbed “dis-crete locally-linear preserving hashing”, which effectively calculates the adjacent matrix while preserving the locally linear structure in the obtained hash space. Specifically, a novel local anchor embedding algorithm is adopted to construct the approximate adjacent matrix. After that, we directly minimize the reconstruction error with the discrete constrain to learn the binary codes. Experimental results on two typical image datasets indicate that the proposed method significantly outperforms the state-of-the-art unsupervised methods.

Hashing for large-scale multimedia is a popular research topic, attracting much attention in computer vision and visual information retrieval. Previous works mostly focus on hashing the images and texts while the approaches designed for videos are limited. In this paper, we propose a \textit{Supervised Recurrent Hashing} (SRH) that explores the discriminative representation obtained by deep neural networks to design hashing approaches. The long-short term memory (LSTM) network is deployed to model the structure of video samples. The max-pooling mechanism is introduced to embedding the frames into fixed-length representations that are fed into supervised hashing loss. Experiments on UCF-101 dataset demonstrate that the proposed method can significantly outperforms several state-of-the-art methods.