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Understanding visual-auditory correlation from heterogeneous features for cross-media retrieval

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第2期9卷 241-249页

作者： Hong ZHANG Yan-yun WANG Hong PAN Fei WU College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China School of Computer Science and Technology Zhejiang University Hangzhou 310027 China School of Elementary Education Hangzhou Normal University Hangzhou 310036 China School of Information Engineering Hangzhou Normal University Hangzhou 310036 China

cross-media retrieval is an interesting research topic,which seeks to remove the barriers among different *** enable cross-media retrieval,it is needed to find the correlation measures between heterogeneous low-level features and to judge the semantic *** paper presents a novel approach to learn cross-media correlation between visual features and auditory features for image-audio retrieval.A semi-supervised correlation preserving mapping(SSCPM)method is described to construct the isomorphic SSCPM subspace where canonical correlations between the original visual and auditory features are further *** optimization algorithm is proposed to improve the local image cluster and audio cluster quality in an interactive way.A unique relevance feedback strategy is developed to update the knowledge of cross-media correlation by learning from user behaviors,so retrieval performance is enhanced in a progressive *** results show that the performance of our approach is effective.

关键词： Heterogeneity cross-media retrieval Subspace optimization Dynamic correlation update

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A Novel Approach Towards Large Scale cross-media retrieval

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Journal of Computer Science & Technology 2012年第6期27卷 1140-1149页

作者：逯波王国仁袁野 College of Information Science and Engineering Northeastern University

With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. Unfortunately, the complexity and the heterogeneity of multi-modality have posed the following two major challenges for cross-media retrieval： 1） how to construct, a unified and compact model for media objects with multi-modality, 2） how to improve the performance of retrieval for large scale cross-media database. In this paper, we propose a novel method which is dedicate to solving these issues to achieve effective and accurate cross-media retrieval. Firstly, a multi-modality semantic relationship graph （MSRG） is constructed using the semantic correlation amongst the media objects with multi-modality. Secondly, all the media objects in MSRG are mapped onto an isomorphic semantic space. Further, an efficient indexing MK-tree based on heterogeneous data distribution is proposed to manage the media objects within the semantic space and improve the performance of cross-media retrieval. Extensive experiments on real large scale cross-media datasets indicate that our proposal dramatically improves the accuracy and efficiency of cross-media retrieval, outperforming the existing methods significantly.

关键词： cross-media retrieval multi-modality semantic correlation indexing structure

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Manifold alignment using discrete surface Ricci flow

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CAAI Transactions on Intelligence Technology 2016年第3期1卷 285-292页

作者： Zhongxin Liu Wenmin Wang Qun Jin School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Faculty of Human Sciences Waseda University Japan

Manifold alignment is useful to extract the shared latent structure among multiple data sets and the similarity among different datasets. As many kinds of real world data can be analyzed using low dimensional representations, manifold alignment algorithms can be used in a wide range of applications, such as data mining. In this paper, we propose a three-stage approach to manifold alignment using discrete surface Ricci flow. Our approach transforms the original intrinsic manifolds to hyper spheres using confonnal mapping in the first stage, and then zooms these hyper spheres into the same scale and aligns them in the following stages. We describe in details about our algorithm, its theoretical principles, our experimental results, and the comparison to previous alignment methods. To prove the effectiveness of our algorithm, three kinds of experiments are presented, including a toy dataset, one containing parallel corpus of parliament proceedings and another containing both images and texts. With these experiments, the latent utility in discovering the similarity among different kinds of data sets can be demonstrated, whether within the same kind of data or across different kinds of modals of data.

关键词： cross-lingual retrieval cross-media retrieval Dimensionality reduction Manifold alignment

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