Modeling Image Context Using Object Centered Grid

Context plays a valuable role in any image understanding task confirmed by numerous studies which have shown the importance of contextual information in computer vision tasks, like object detection, scene classification and image retrieval. Studies of human perception on the tasks of scene classification and visual search have shown that human visual system makes extensive use of contextual information as postprocessing in order to index objects. Several recent computer vision approaches use contextual information to improve object recognition performance. They mainly use global information of the whole image by dividing the image into several predefined subregions, so called fixed grid. In this paper we propose an alternative approach to retrieval of contextual information, by customizing the location of the grid based on salient objects in the image. We claim this approach to result in more informative contextual features compared to the fixed grid based strategy. To compare our results with the most relevant and recent papers, we use PASCAL 2007 data set. Our experimental results show an improvement in terms of Mean Average Precision.