Video retrieval based on deep convolutional neural network

Recently, with the enormous growth of online videos, fast video retrieval research has received increasing attention. As an extension of image hashing techniques, traditional video hashing methods mainly depend on hand-crafted features and transform the real-valued features into binary hash codes. As videos provide far more diverse and complex visual information than images, extracting features from videos is much more challenging than that from images. Therefore, high-level semantic features to represent videos are needed rather than low-level hand-crafted methods. In this paper, a deep convolutional neural network is proposed to extract high-level semantic features and a binary hash function is then integrated into this framework to achieve an end-to-end optimization. Particularly, our approach also combines triplet loss function which preserves the relative similarity and difference of videos and classification loss function as the optimization objective. Experiments have been performed on two public datasets and the results demonstrate the superiority of our proposed method compared with other state-of-the-art video retrieval methods

Deep Binary Reconstruction for Cross-modal Hashing

With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the binary codes of one modality as the query to retrieve the relevant hashing codes of another modality. However, the existing binary constraint makes it difficult to find the optimal cross-modal hashing function. Most approaches choose to relax the constraint and perform thresholding strategy on the real-value representation instead of directly solving the original objective. In this paper, we first provide a concrete analysis about the effectiveness of multimodal networks in preserving the inter- and intra-modal consistency. Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion. The superiority comes from a proposed simple but efficient activation function, named as Adaptive Tanh (ATanh). The ATanh function can adaptively learn the binary codes and be trained via back-propagation. Extensive experiments on three benchmark datasets demonstrate that DBRC outperforms several state-of-the-art methods in both image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201

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