Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization

By Zhou Zhao, Hanqing Lu, Deng Cai, Xiaofei He, Yueting Zhuang. 2016

12 citations recorded by the source corpus.

Multi-modal sparse coding has played an important role in many multimedia applications, where data are usually with multiple modalities. Recently, various multi-modal sparse coding approaches have been proposed to learn sparse codes of multi-modal data, which assume that data appear in all modalities, or at least there is one modality containing all data. However, in real applications, it is often the case that some modalities of the data may suffer from missing information and thus result in partial multi-modality data. In this paper, we propose to solve the partial multi-modal sparse coding problem via multi-modal similarity structure regularization. Specifically, we propose a partial multi-modal sparse coding framework termed Adaptive Partial Multi-Modal Similarity Structure Regularization for Sparse Coding (AdaPM2SC), which preserves the similarity structure within the same modality and between different modalities. Experimental results conducted on two real-world datasets demonstrate that AdaPM2SC significantly outperforms the state-of-the-art methods under partial multi-modality scenario.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Tat-seng Chua
  2. Xiangnan He
  3. Hanwang Zhang
  4. Cordelia Schmid
  5. Liqiang Nie
  6. Dacheng Tao
  7. Yueting Zhuang
  8. Jun Xiao

In Signal Processing

  1. Liqiang Nie
  2. Tat-seng Chua
  3. Wei David Liu
  4. Zhou Zhao
  5. Hanwang Zhang
  6. Xiangnan He
  7. Yi Ren
  8. Jinglin Liu

Citations in the corpus