In this paper, we investigate how to iteratively and mutually boost object classification and detection by taking the outputs from one task as the context of the other one. First, instead of intuitive feature and context concatenation or postprocessing with context, the so-called Contextualized Support Vector Machine (Context-SVM) is proposed, where the context takes the responsibility of dynamically adjusting the classification hyperplane, and thus the context-adaptive classifier is achieved. Then, an iterative training procedure is presented. In each step, Context-SVM, associated with the output context from one task (object classification or detection), is instantiated to boost the performance for the other task, whose augmented outputs are then further used to improve the former task by Context-SVM. The proposed solution is evaluated on the object classification and detection tasks of PASCAL Visual Object Challenge (VOC) 2007 and 2010, and achieves the state-of-the-art performance.
Another work REF has proposed a learning framework of object detection and classification by concatenation of feature and context information using Support Vector Machine (Context-SVM).
Contextualizing object detection and classification
CVPR 2011
CVPR 2011
Computer Science