As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework. First, we design a two-pathway deep network pretrained with contrastive loss, and employ double triplet similarity loss for fine-tuning to learn the shared representation for each media type by modeling the relative semantic similarity. Second, the metric network is designed for effectively calculating the cross-media similarity of the shared representation, by modeling the pairwise similar and dissimilar constraints. Compared to the existing methods which mostly ignore the dissimilar constraints and only use sample distance metric as Euclidean distance separately, our UNCSM approach unifies the representation learning and distance metric to preserve the relative similarity as well as embrace more complex similarity functions for further improving the cross-media retrieval accuracy. The experimental results show that our UNCSM approach outperforms 8 state-of-the-art methods on 4 widely-used cross-media datasets.
As for the first strategy, it mainly uses linear function to project the multimedia data into one common space. For example, a straightforward method is to adopt Canonical Correlation Analysis (CCA) @cite_13 , which is a traditional statistical correlation analysis method, to project the features of different media types into a lower-dimensional common space. Given the training pairs, CCA can find matrices for them, which can make the projected training pairs have maximum correlations with the same dimension to obtain the shared representation. Thus, the simple similarity measurement can be adopted to present cross-media retrieval. Some later works attempt to combine semantic categories to extend CCA, such as @cite_6 . Besides, Cross-modal Factor Analysis (CFA) @cite_20 minimizes the Frobenius norm between pairwise data in the transformed domain to learn a common space for different modalities. The joint graph regularized heterogeneous metric learning (JGRHML) @cite_1 is proposed by to construct the joint graph regularization term using the data in the learned metric space, and this work is further improved as the joint representation learning (JRL) @cite_29 by modeling the correlations and semantic information in a unified framework.
@cite_29 @cite_1 @cite_6 @cite_13 @cite_20
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Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query. The state-of-the-art approach to cross-modality retrieval relies on learning a joint embedding space of the two modalities, where items from either modality are retrieved using nearest-neighbor search. In this work, we introduce a neural network layer based on canonical correlation analysis (CCA) that learns better embedding spaces by analytically computing projections that maximize correlation. In contrast to previous approaches, the CCA layer allows us to combine existing objectives for embedding space learning, such as pairwise ranking losses, with the optimal projections of CCA. We show the effectiveness of our approach for cross-modality retrieval on three different scenarios (text-to-image, audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a multi-view network using freely learned projections optimized by a pairwise ranking loss, especially when little training data is available (the code for all three methods is released at: https: github.com CPJKU cca_layer). 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. Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challenge. Existing cross-media retrieval methods usually focus on modeling the pairwise correlation or semantic information separately. In fact, these two kinds of information are complementary to each other and optimizing them simultaneously can further improve the accuracy. In this paper, we propose a novel feature learning algorithm for cross-media data, called joint representation learning (JRL), which is able to explore jointly the correlation and semantic information in a unified optimization framework. JRL integrates the sparse and semisupervised regularization for different media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type. On one hand, JRL learns sparse projection matrix for different media simultaneously, so different media can align with each other, which is robust to the noise. On the other hand, both the labeled data and unlabeled data of different media types are explored. Unlabeled examples of different media types increase the diversity of training data and boost the performance of joint representation learning. Furthermore, JRL can not only reduce the dimension of the original features, but also incorporate the cross-media correlation into the final representation, which further improves the performance of both cross-media retrieval and single-media retrieval. Experiments on two datasets with up to five media types show the effectiveness of our proposed approach, as compared with the state-of-the-art methods. In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, wheretheclass labelsdefinecorrespondencesbetween the sets. Cluster-CCA is able to learn discriminant low dimensional representations that maximize the correlation between the two sets while segregating the different classes on the learned space. Furthermore, we present a kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) that extends clusterCCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to standard (K)CCA. By means of experimental evaluation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks.