Hashing has recently sparked a great revolution in cross-modal retrieval due to its low storage cost and high query speed. Most existing cross-modal hashing methods learn unified hash codes in a common Hamming space to represent all multi-modal data and make them intuitively comparable. However, such unified hash codes could inherently sacrifice their representation scalability because the data from different modalities may not have one-to-one correspondence and could be stored more efficiently by different hash codes of unequal lengths. To mitigate this problem, this paper proposes a generalized and flexible cross-modal hashing framework, termed Matrix Tri-Factorization Hashing (MTFH), which not only preserves the semantic similarity between the multi-modal data points, but also works seamlessly in various settings including paired or unpaired multi-modal data, and equal or varying hash length encoding scenarios. Specifically, MTFH exploits an efficient objective function to jointly learn the flexible modality-specific hash codes with different length settings, while simultaneously excavating two semantic correlation matrices to ensure heterogeneous data comparable. As a result, the derived hash codes are more semantically meaningful for various challenging cross-modal retrieval tasks. Extensive experiments evaluated on public benchmark datasets highlight the superiority of MTFH under various retrieval scenarios and show its very competitive performance with the state-of-the-arts.
Hashing technique @cite_16 @cite_4 @cite_15 , favored for its low storage cost and fast query speed, have recently attracted much attention in cross-modal retrieval domain. Most prior hashing works mainly concentrate on producing binary codes for data within the same modality @cite_32 @cite_24 , , locality sensitive hashing (LSH) @cite_16 and its kernelized extension @cite_24 , spectral hashing @cite_35 and k-means hashing (KMH) @cite_29 . These hashing methods provide important theoretical foundations for cross-modal hashing, whose main challenge is to learn compact binary codes that can construct the underlying correlations between heterogeneous modalities. In the literature, existing cross-modal hashing methods mainly fall into the modality-independent and modality-dependent branches. Modality-independent approaches first exploit the separate hash codes and learn the corresponding hash functions for different modalities individually @cite_26 @cite_43 . Then, the integrated hash codes a multi-modal instance is directly constructed by concatenating the individual hash codes from each modality. A representative work is cross-view hashing (CVH) @cite_43 , which attempts to learn the independent hash codes of different modalities while minimizing the similarity-weighted hamming distances between them. However, this method often builds a weak connection between heterogeneous modalities and its corresponding retrieval performance needs further improvement.
@cite_35 @cite_26 @cite_4 @cite_29 @cite_32 @cite_24 @cite_43 @cite_15 @cite_16
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Similarity search methods based on hashing for effective and efficient cross-modal retrieval on large-scale multimedia databases with massive text and images have attracted considerable attention. The core problem of cross-modal hashing is how to effectively construct correlation between multi-modal representations which are heterogeneous intrinsically in the process of hash function learning. Analogous to Canonical Correlation Analysis (CCA), most existing cross-modal hash methods embed the heterogeneous data into a joint abstraction space by linear projections. However, these methods fail to bridge the semantic gap more effectively, and capture high-level latent semantic information which has been proved that it can lead to better performance for image retrieval. To address these challenges, in this paper, we propose a novel Latent Semantic Sparse Hashing (LSSH) to perform cross-modal similarity search by employing Sparse Coding and Matrix Factorization. In particular, LSSH uses Sparse Coding to capture the salient structures of images, and Matrix Factorization to learn the latent concepts from text. Then the learned latent semantic features are mapped to a joint abstraction space. Moreover, an iterative strategy is applied to derive optimal solutions efficiently, and it helps LSSH to explore the correlation between multi-modal representations efficiently and automatically. Finally, the unified hashcodes are generated through the high level abstraction space by quantization. Extensive experiments on three different datasets highlight the advantage of our method under cross-modal scenarios and show that LSSH significantly outperforms several state-of-the-art methods. By transforming data into binary representation, i.e., Hashing, we can perform high-speed search with low storage cost, and thus, Hashing has collected increasing research interest in the recent years. Recently, how to generate Hashcode for multimodal data (e.g., images with textual tags, documents with photos, and so on) for large-scale cross-modality search (e.g., searching semantically related images in database for a document query) is an important research issue because of the fast growth of multimodal data in the Web. To address this issue, a novel framework for multimodal Hashing is proposed, termed as Collective Matrix Factorization Hashing (CMFH). The key idea of CMFH is to learn unified Hashcodes for different modalities of one multimodal instance in the shared latent semantic space in which different modalities can be effectively connected. Therefore, accurate cross-modality search is supported. Based on the general framework, we extend it in the unsupervised scenario where it tries to preserve the Euclidean structure, and in the supervised scenario where it fully exploits the label information of data. The corresponding theoretical analysis and the optimization algorithms are given. We conducted comprehensive experiments on three benchmark data sets for cross-modality search. The experimental results demonstrate that CMFH can significantly outperform several state-of-the-art cross-modality Hashing methods, which validates the effectiveness of the proposed CMFH. Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO. Hashing has gained considerable attention on large-scale similarity search, due to its enjoyable efficiency and low storage cost. In this paper, we study the problem of learning hash functions in the context of multi-modal data for cross-modal similarity search. Notwithstanding the progress achieved by existing methods, they essentially learn only one common hamming space, where data objects from all modalities are mapped to conduct similarity search. However, such method is unable to well characterize the flexible and discriminative local (neighborhood) structure in all modalities simultaneously, hindering them to achieve better performance. Bearing such stand-out limitation, we propose to learn heterogeneous hamming spaces with each preserving the local structure of data objects from an individual modality. Then, a novel method to learning bridging mapping for cross-modal hashing, named LBMCH, is proposed to characterize the cross-modal semantic correspondence by seamlessly connecting these distinct hamming spaces. Meanwhile, the local structure of each data object in a modality is preserved by constructing an anchor based representation, enabling LBMCH to characterize a linear complexity w.r.t the size of training set. The efficacy of LBMCH is experimentally validated against real-world cross-modal datasets.