Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the inter-modal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semi-supervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multi-modal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.
For the rapid growth of data volume, the cost of finding nearest neighbor cannot be dismissed. Hashing is a scalable method for finding nearest neighbors approximately @cite_15 . It projects data into Hamming space, where the neighbor search can be performed efficiently. To improve the efficient of finding similar multi-modal objects, many cross-modal hashing methods have been proposed @cite_15 @cite_0 @cite_3 @cite_34 @cite_29 . Kumar and Udupa @cite_0 proposed a cross view hashing method to generate such hash codes that minimized the distance in Hamming space between similar objects and maximized that between dissimilar ones. @cite_3 used a co-regularization framework to generate such binary code that the hash codes from different modality were consistent. @cite_34 constructed a Hamming space for each modality and build the mapping between them with logistic regression. @cite_29 proposed a sparse multi-modal hashing method for cross-modal retrieval.
@cite_29 @cite_3 @cite_0 @cite_15 @cite_34
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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. Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied up to any specific form of loss function, which is typical for existing cross-modal hashing methods, but rather we can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with on 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. Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary hash functions for fast nearest neighbor retrieval. The nearest neighbors are defined according to the semantic similarity between the objects. Our method uses the information of these semantic similarities and learns a hash function with binary code such that only objects with high similarity have small Hamming distance. The hash function is incrementally trained one bit at a time, and as bits are added to the hash code Hamming distances between dissimilar objects increase. We further link our method to the idea of maximizing conditional entropy among pair of bits and derive an extremely efficient linear time hash learning algorithm. Experiments on similar image retrieval and celebrity face recognition show that our method produces apparent improvement in performance over some state-of-the-art methods. 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.