Building multiple hash tables serves as a very successful technique for gigantic data indexing, which can simultaneously guarantee both the search accuracy and efficiency. However, most of existing multitable indexing solutions, without informative hash codes and strong table complementarity, largely suffer from the table redundancy. To address the problem, we propose a complementary binary quantization (CBQ) method for jointly learning multiple tables and the corresponding informative hash functions in a centralized way. Based on CBQ, we further design a distributed learning algorithm (D-CBQ) to accelerate the training over the large-scale distributed data set. The proposed (D-)CBQ exploits the power of prototype-based incomplete binary coding to well align the data distributions in the original space and the Hamming space and further utilizes the nature of multi-index search to jointly reduce the quantization loss. (D-)CBQ possesses several attractive properties, including the extensibility for generating long hash codes in the product space and the scalability with linear training time. Extensive experiments on two popular large-scale tasks, including the Euclidean and semantic nearest neighbor search, demonstrate that the proposed (D-)CBQ enjoys efficient computation, informative binary quantization, and strong table complementarity, which together help significantly outperform the state of the arts, with up to 57.76% performance gains relatively.

Image hashing is critical for large-scale image analytic-based applications, such as image retrieval. Although there have been dozens of hashing approaches, few of them take the hierarchical structure of the image categories into consideration. In this paper, we propose to incorporate the category taxonomy information in a deep reinforcement learning (DRL) model for image hashing. In particular, we learn an agent to predict the hashing codes sequentially under the DRL theme. Each coordinate of the hashing function can take the errors incurred by previous ones into consideration and hence more reliable hashing codes can be obtained than learning them independently. Besides, we design a novel level-specific reward function to gradually refine the hashing function according to the taxonomy information. Extensive experiments on two popular datasets demonstrate effectiveness of the proposed method.

Building multiple hash tables has been proven a successful technique for indexing massive databases, which can guarantee a desired level of overall performance. However, existing hash based multi-indexing methods suffer from the heavy redundancy, without strong table complementarity and effective hash code learning. To address the problems, this paper proposes a complementary binary quantization (CBQ) method to jointly learning multiple hash tables. It exploits the power of incomplete binary coding based on prototypes to align the original space and the Hamming space, and further utilizes the nature of multi-indexing search to jointly reduce the quantization loss based on the prototype based hash function. Our alternating optimization adaptively discovers the complementary prototype sets and the corresponding code sets of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes. Extensive experiments carried out on two popular large-scale tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed CBQ method enjoys the strong table complementarity and significantly outperforms the state-of-the-art, with up to 57.76\% performance gains relatively.

It is desired (especially for young people) to shop for the same or similar products shown in the multimedia contents (such as online TV programs). This indicates an urgent demand for improving the experience of TV-to-Online (T2O). In this paper, a transfer learning approach as well as a prototype system for effortless T2O experience is developed. In the system, a key component is high-precision product search, which is to fulfill exact matching between a query item and the database ones. The matching performance primarily relies on distance estimation, but the data characteristics cannot be well modeled and exploited by a simple Euclidean distance. This motivates us to introduce distance metric learning (DML) for improving the distance estimation. However, in traditional DML methods, the side information (such as the similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain is leveraged. These methods may fail due to limited side information. Fortunately, this issue can be alleviated by utilizing transfer metric learning (TML) to exploit information from other related domains. In this paper, a novel manifold regularized heterogeneous multitask metric learning framework is proposed, in which each domain is treated equally. The proposed approach allows us to simultaneously exploit the information from other domains and the unlabeled information. Furthermore, the ranking-based loss is adopted to make our model more appropriate for search. Experiments on two challenging real-world datasets demonstrate the effectiveness of the proposed method. This TML approach is expected to impact the transformation of the emerging T2O trend in both TV and online video domains.

Shopping of the same or similar types of products as shown in the online TV programs has been highly desired by many people, especially the youth. To meet this eminent market need, we develop a prototype system to enable effortless TV-to-Online (T2O) experience. A key component of this system is the product search that maps specific items embedded in the video into a list of online merchants. The search performance mainly depends on the estimation of the distance or similarity between the queried item and all the curated items in the database. The simple Euclidean (EU) distance cannot capture the data characteristics, we therefore introduce distance metric learning (DML) to improve the distance estimation. Traditional DML methods only utilize the side information (e.g., similar/dissimilar constraints or relevance/irrelevance judgements) in the target domain, and may fail when the side information is scarce. Transfer metric learning (TML) can be adopted to leverage the side information from related domains. In this paper, we treat each domain equally and propose a novel Ranking-based Heterogeneous Multi-Task Metric Learning (RHMTML) framework, which adopts ranking-based loss, so the learned metric is particularly suitable for search. Extensive experiments demonstrate the effectiveness of our proposed method. We foresee that our approach would transform the emerging T2O trend in both TV and online video market.