MESH : a flexible manifold-embedded semantic hashing for cross-modal retrieval

Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this article, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method. © 2013 IEEE

On Aggregation of Unsupervised Deep Binary Descriptor with Weak Bits

Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations: 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ2,1-norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts

Learning effective binary representation with deep hashing technique for large-scale multimedia similarity search

The explosive growth of multimedia data in modern times inspires the research of performing an efficient large-scale multimedia similarity search in the existing information retrieval systems. In the past decades, the hashing-based nearest neighbor search methods draw extensive attention in this research field. By representing the original data with compact hash code, it enables the efficient similarity retrieval by only conducting bitwise operation when computing the Hamming distance. Moreover, less memory space is required to process and store the massive amounts of features for the search engines owing to the nature of compact binary code. These advantages make hashing a competitive option in large-scale visual-related retrieval tasks. Motivated by the previous dedicated works, this thesis focuses on learning compact binary representation via hashing techniques for the large-scale multimedia similarity search tasks. Particularly, several novel frameworks are proposed for popular hashing-based applications like a local binary descriptor for patch-level matching (Chapter 3), video-to-video retrieval (Chapter 4) and cross-modality retrieval (Chapter 5). This thesis starts by addressing the problem of learning local binary descriptor for better patch/image matching performance. To this end, we propose a novel local descriptor termed Unsupervised Deep Binary Descriptor (UDBD) for the patch-level matching tasks, which learns the transformation invariant binary descriptor via embedding the original visual data and their transformed sets into a common Hamming space. By imposing a l2,1-norm regularizer on the objective function, the learned binary descriptor gains robustness against noises. Moreover, a weak bit scheme is applied to address the ambiguous matching in the local binary descriptor, where the best match is determined for each query by comparing a series of weak bits between the query instance and the candidates, thus improving the matching performance. Furthermore, Unsupervised Deep Video Hashing (UDVH) is proposed to facilitate large-scale video-to-video retrieval. To tackle the imbalanced distribution issue in the video feature, balanced rotation is developed to identify a proper projection matrix such that the information of each dimension can be balanced in the fixed-bit quantization, thus improving the retrieval performance dramatically with better code quality. To provide comprehensive insights on the proposed rotation, two different video feature learning structures: stacked LSTM units (UDVH-LSTM) and Temporal Segment Network (UDVH-TSN) are presented in Chapter 4. Lastly, we extend the research topic from single-modality to cross-modality retrieval, where Self-Supervised Deep Multimodal Hashing (SSDMH) based on matrix factorization is proposed to learn unified binary code for different modalities directly without the need for relaxation. By minimizing graph regularization loss, it is prone to produce discriminative hash code via preserving the original data structure. Moreover, Binary Gradient Descent (BGD) accelerates the discrete optimization against the bit-by-bit fashion. Besides, an unsupervised version termed Unsupervised Deep Cross-Modal Hashing (UDCMH) is proposed to tackle the large-scale cross-modality retrieval when prior knowledge is unavailable