199 research outputs found
Hetero-manifold Regularisation for Cross-modal Hashing
Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks
Deep Heterogeneous Hashing for Face Video Retrieval
Retrieving videos of a particular person with face image as a query via
hashing technique has many important applications. While face images are
typically represented as vectors in Euclidean space, characterizing face videos
with some robust set modeling techniques (e.g. covariance matrices as exploited
in this study, which reside on Riemannian manifold), has recently shown
appealing advantages. This hence results in a thorny heterogeneous spaces
matching problem. Moreover, hashing with handcrafted features as done in many
existing works is clearly inadequate to achieve desirable performance for this
task. To address such problems, we present an end-to-end Deep Heterogeneous
Hashing (DHH) method that integrates three stages including image feature
learning, video modeling, and heterogeneous hashing in a single framework, to
learn unified binary codes for both face images and videos. To tackle the key
challenge of hashing on the manifold, a well-studied Riemannian kernel mapping
is employed to project data (i.e. covariance matrices) into Euclidean space and
thus enables to embed the two heterogeneous representations into a common
Hamming space, where both intra-space discriminability and inter-space
compatibility are considered. To perform network optimization, the gradient of
the kernel mapping is innovatively derived via structured matrix
backpropagation in a theoretically principled way. Experiments on three
challenging datasets show that our method achieves quite competitive
performance compared with existing hashing methods.Comment: 14 pages, 17 figures, 4 tables, accepted by IEEE Transactions on
Image Processing (TIP) 201
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