title: Submodular Attribute Selection for Visual Recognition.
abstract: In real-world visual recognition problems, low-level features cannot adequately characterize the semantic content in images, or the spatio-temporal structure in videos. In this work, we encode objects or actions based on attributes that describe them as high-level concepts. We consider two types of attributes. One type of attributes is generated by humans, while the second type is data-driven attributes extracted from data using dictionary learning methods. Attribute-based representation may exhibit variations due to noisy and redundant attributes. We propose a discriminative and compact attribute-based representation by selecting a subset of discriminative attributes from a large attribute set. Three attribute selection criteria are proposed and formulated as a submodular optimization problem. A greedy optimization algorithm is presented and its solution is guaranteed to be at least (1-1/e)-approximation to the optimum. Experimental results on four public datasets demonstrate that the proposed attribute-based representation significantly boosts the performance of visual recognition and outperforms most recently proposed recognition approaches.
authors.lastname: Jingjing Zheng
authors.lastname: Zhuolin Jiang
authors.forename: Rama
authors.initials: R
authors.lastname: Chellappa
mesh_terms: D000465:Algorithms; D000818:Animals; D016208:Databases, Factual; D006802:Human Activities / Q000145:classification; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013177:Sports / Q000145:classification; D014741:Video Recording