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.
Modality-dependent approaches mainly learn the unified hash codes to characterize the multi-modal data and simultaneously exploit the hashing mapping functions across different modalities, which can be generally categorized into unsupervised and supervised branches. Without semantic label supervision, unsupervised cross-modal hashing generally learns the hash codes from original feature space to Hamming space in an intuitive way. For instance, inter-media hashing (IMH) @cite_8 first exploits the intra-view and inter-view consistency in a common Hamming space, and then learns the hash mapping functions via linear regression to generate the hash codes. Collective matrix factorization hashing (CMFH) @cite_18 utilizes the joint matrix factorization to learn the unified hash codes for varying multi-modal data, while latent semantic sparse hashing (LSSH) @cite_42 produces a unified hash code via the latent semantic sparse representation. Although these methods are able to capture the semantic correlations between heterogeneous modalities, the hash codes learned in an unsupervised way are not discriminative enough and the corresponding cross-modal similarity is not well preserved in the Hamming space. Consequently, these approaches are restricted by the semantic gap that the high-level semantic hash description of a data sample differs from its low-level feature descriptor, which may result in a poor cross-modal retrieval performance.
@cite_18 @cite_42 @cite_8
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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. Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively weak'' modalities into DCDH and extend it to multi-view DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV-DCDH outperform the state-of-the-art methods significantly on cross-media retrieval. Multimodal hashing is essential to cross-media similarity search for its low storage cost and fast query speed. Most existing multimodal hashing methods embedded heterogeneous data into a common low-dimensional Hamming space, and then rounded the continuous embeddings to obtain the binary codes. Yet they usually neglect the inherent discrete nature of hashing for relaxing the discrete constraints, which will cause degraded retrieval performance especially for long codes. For this purpose, a novel Semantic Topic Multimodal Hashing (STMH) is developed by considering latent semantic information in coding procedure. It first discovers clustering patterns of texts and robust factorizes the matrix of images to obtain multiple semantic topics of texts and concepts of images. Then the learned multimodal semantic features are transformed into a common subspace by their correlations. Finally, each bit of unified hash code can be generated directly by figuring out whether a topic or concept is contained in a text or an image. Therefore, the obtained model by STMH is more suitable for hashing scheme as it directly learns discrete hash codes in the coding process. Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods.