Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding

Chenxiao Li; Guiguang Ding; Jile Zhou; Yuchen Guo; Qiang Liu

doi:10.1117/12.2073276

4 November 2014 Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding

Chenxiao Li, Guiguang Ding, Jile Zhou, Yuchen Guo, Qiang Liu

Proceedings Volume 9273, Optoelectronic Imaging and Multimedia Technology III; 92731K (2014) https://doi.org/10.1117/12.2073276
Event: SPIE/COS Photonics Asia, 2014, Beijing, China

Abstract

Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically, sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their common sparse latent semantic structure so that the learned visual dictionary is able to describe both the visual and textual properties of training data. Experiments on both image categorization and retrieval tasks demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse Coding.

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Chenxiao Li, Guiguang Ding, Jile Zhou, Yuchen Guo, and Qiang Liu "Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731K (4 November 2014); https://doi.org/10.1117/12.2073276

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KEYWORDS

Associative arrays

Visualization

Image compression

Visual process modeling

Image retrieval

Data modeling

Computer programming

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