FISHER-SELECTIVE SEARCH FOR OBJECT DETECTION

An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning.