Issue No. 02 - April-June (2012 vol. 19)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MMUL.2011.20
Songde Ma , Nat. Lab. of Pattern Recognition, China
Hanqing Lu , Nat. Lab. of Pattern Recognition, China
Qi Tian , Univ. of Texas at San Antonio, San Antonio, TX, USA
Jing Liu , Nat. Lab. of Pattern Recognition, China
Chunjie Zhang , Nat. Lab. of Pattern Recognition, China
Using higher-level visual elements to represent images, the authors have developed a sparsity-constrained bilinear model (SBLM) and have combined a set of SBLMs in a boosting-like procedure to enhance performance.
object recognition, image representation, SBLM, sparsity-constrained bilinear model, object recognition, higher-level visual elements, image representation, boosting-like procedure, Visualization, Image processing, Object recognition, Image representation, Robustness, Adaptation model, Video communication, Information retrieval, Computer vision, image/video retrieval, multimedia, computer vision, object recognition, image processing
Songde Ma, Yanjun Han, Hanqing Lu, Qi Tian, Jing Liu and Chunjie Zhang, "A Boosting, Sparsity- Constrained Bilinear Model for Object Recognition," in IEEE MultiMedia, vol. 19, no. , pp. 58-68, 2012.