Heterogeneous Visual Features Fusion via Sparse Multimodal Machine

  title={Heterogeneous Visual Features Fusion via Sparse Multimodal Machine},
  author={Hua Wang and Feiping Nie and Heng Huang and Chris H. Q. Ding},
  journal={2013 IEEE Conference on Computer Vision and Pattern Recognition},
To better understand, search, and classify image and video information, many visual feature descriptors have been proposed to describe elementary visual characteristics, such as the shape, the color, the texture, etc. How to integrate these heterogeneous visual features and identify the important ones from them for specific vision tasks has become an increasingly critical problem. In this paper, We propose a novel Sparse Multimodal Learning (SMML) approach to integrate such heterogeneous… CONTINUE READING
Highly Cited
This paper has 92 citations. REVIEW CITATIONS

4 Figures & Tables



Citations per Year

92 Citations

Semantic Scholar estimates that this publication has 92 citations based on the available data.

See our FAQ for additional information.