Techniques for still image scene classification and object detection
In this paper we consider the interaction between different semantic levels in still image scene classification and object detection problems. We present a method where a neural method is used to produce a tentative higher-level semantic scene representation from low-level statistical visual features in a bottom-up fashion. This emergent representation is then used to refine the lower-level object detection results. We evaluate the proposed method with data from Pascal VOC Challenge 2006 image classification and object detection competition. The proposed techniques for exploiting global classification results are found to significantly improve the accuracy of local object detection.