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Your search keyword '"Wu, Fei"' showing total 26 results
Start Over Author "Wu, Fei" Journal ieee transactions on image processing
26 results on '"Wu, Fei"'

25. Cross-Modal Learning to Rank via Latent Joint Representation.

Author

Wu, Fei, Jiang, Xinyang, Li, Xi, Tang, Siliang, Lu, Weiming, Zhang, Zhongfei, and Zhuang, Yueting

Subjects
*MACHINE learning, *RANKING (Statistics), *IMAGE representation, *IMAGE analysis, *QUERY (Information retrieval system)
Abstract

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML ^2\textR ). In CML ^2\textR , the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data. [ABSTRACT FROM AUTHOR]

Published
2015
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26. Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding.

Author

Yang, Yi, Wu, Fei, Nie, Feiping, Shen, Heng Tao, Zhuang, Yueting, and Hauptmann, Alexander G.

Subjects
*DIGITAL images, *EMBEDDINGS (Mathematics), *STATISTICAL correlation, *ALGORITHMS, *IMAGE quality analysis, *IMAGE databases
Abstract

The number of digital images rapidly increases, and it becomes an important challenge to organize these resources effectively. As a way to facilitate image categorization and retrieval, automatic image annotation has received much research attention. Considering that there are a great number of unlabeled images available, it is beneficial to develop an effective mechanism to leverage unlabeled images for large-scale image annotation. Meanwhile, a single image is usually associated with multiple labels, which are inherently correlated to each other. A straightforward method of image annotation is to decompose the problem into multiple independent single-label problems, but this ignores the underlying correlations among different labels. In this paper, we propose a new inductive algorithm for image annotation by integrating label correlation mining and visual similarity mining into a joint framework. We first construct a graph model according to image visual features. A multilabel classifier is then trained by simultaneously uncovering the shared structure common to different labels and the visual graph embedded label prediction matrix for image annotation. We show that the globally optimal solution of the proposed framework can be obtained by performing generalized eigen-decomposition. We apply the proposed framework to both web image annotation and personal album labeling using the NUS-WIDE, MSRA MM 2.0, and Kodak image data sets, and the AUC evaluation metric. Extensive experiments on large-scale image databases collected from the web and personal album show that the proposed algorithm is capable of utilizing both labeled and unlabeled data for image annotation and outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Published
2012
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