Modeling Visual Patterns by Integrating Descriptive and Generative Methods

@misc{citeulike:3014089, abstract = {This paper presents a class of statistical models that integrate two statistical modeling paradigms in the literature: I). Descriptive methods, such as Markov random elds and minimax entropy learning [41], and II). Generative methods, such as principal component analysis, independent component analysis [2], transformed component analysis [11], wavelet coding [27, 5], and sparse coding [30, 24]. In this paper, we demonstrate the integrated framework by constructing a class of hierarchical...}, author = {Guo, Cheng E. and Zhu, Song C. and Wu, Ying N. }, citeulike-article-id = {3014089}, keywords = {computer\_vision}, posted-at = {2008-07-17 11:43:45}, priority = {2}, title = {Modeling Visual Patterns by Integrating Descriptive and Generative Methods}, url = {http://citeseer.ist.psu.edu/637117.html} }

TY - GEN ID - citeulike:3014089 L3 - citeulike-article-id:3014089 TI - Modeling Visual Patterns by Integrating Descriptive and Generative Methods N2 - This paper presents a class of statistical models that integrate two statistical modeling paradigms in the literature: I). Descriptive methods, such as Markov random elds and minimax entropy learning [41], and II). Generative methods, such as principal component analysis, independent component analysis [2], transformed component analysis [11], wavelet coding [27, 5], and sparse coding [30, 24]. In this paper, we demonstrate the integrated framework by constructing a class of hierarchical... KW - computer_vision AU - Guo, Cheng AU - Zhu, Song AU - Wu, Ying UR - http://citeseer.ist.psu.edu/637117.html ER -