Multi-information for Visual Object Categorization
基于多信息融合的视觉目标类识别算法研究

Visual object categorization(VOC) is one of the most difficult challenges in computer vision. Spatial pyramid histogram has been proposedd in recent years as an effective way to deal with features sets. However, there remains a large space for improvement. We made use of the respective advantage of spatial pyramid histogram and fisher score representation and proposed to use multi-information for recognition from information complement point view. The experiment results confirm our strategy, and our proposed algorithm consistently boosts the performance of all classes compared with their respective performances.