Learning Visual Object and Word Association

• Main Authors: Ting-Zhi Wang, 王亭之
• Other Authors: Yie-Tarng Chen
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/91551219851515561995

碩士 === 國立臺灣科技大學 === 電子工程系 === 104 === This paper presents a discriminative learning framework to simultaneously find the association between objects and words and perform template matching for complex association patterns. We formulate the problem of finding the association between visual objects and texts as a bipartite graph matching problem. Since the compatibility function has significant influence on the final matching results, we learn an optimal compatibility function which encodes the association rules for visual objects and words via a structural support vector machine (SVM). Also, an iterative inference procedure is developed to alternatively infer visual objects and texts association and template model selection. Simulations show the new method outperforms some competing counterparts.