Selective Search Method for Object Localization and Detection using Wavelets and Hierarchical Segmentations

Abstract

This article proposes a selective search method for object localization in natural images by applying image multi-segmentation, image scaling, and heuristics. The method increases the number of generated windows that delimitate the area of an object with an accuracy superior to 50%. Over-segmentation is applied on original size images in order to locate small objects, and it is also applied over scaled images because these can still be over-segmented. This process produces less regions on areas with many textures. The over-segmentation was applied using the CIE Luv color model, and using the H and the I channels of the HSI model. The proposed method is category independent and allows the location of objects with heterogeneous characteristics by using heuristics and hierarchical segmentation. The proposed method produces 9, 366 windows per image covering 96.78% of the objects in the PASCAL VOC 2007 test image collection, increasing in 0.8% the localization results reported in the state of the art.