Bounding Box Object Localization Based On Image Superpixelization

One of successful approaches for object localization and recognition is sliding window approach where different candidate windows are found and evaluated and the best window is selected to represent the object. To avoid exhaustive search over all windows locations, Efficient Sub-window Search (ESS) [1] algorithm were proposed to efficiently find the best window among all sub-windows using branch and bound technique. In case of multi-class multiple object detection problems, such methods are time consuming. To handle this issue, efficient multi-object detection approach is proposed based on image superpixelization We utilize image superpixels in 2 points. (a) Such preprocessing stage is done once for an image, hence multiple detections could be fast. (b) We observe that image superpixels could help in identifying the promising candidate sub-windows, hence evaluating all sub-windows could be avoided. An efficient brute force sub-window search algorithm is proposed based on these observations. Moreover, for improving its performance, the algorithm is integrated with ESS algorithm. The proposed algorithms are assessed on the PASCAL 2006 and PASCAL 2007 test-set and show that they are faster than the state-of-the art sub-window search algorithms, while achieving a comparable performance comparing with them.