Active Mask Hierarchies for Object Detection

This paper presents a new object representation, Active Mask Hierarchies (AMH), for object detection. In this representation, an object is described using a mixture of hierarchical trees where the nodes represent the object and its parts in pyramid form. To account for shape variations at a range of scales, a dictionary of masks with varied shape patterns are attached to the nodes at different layers. The shape masks are “active” in that they enable parts to move with different displacements. The masks in this active hierarchy are associated with histograms of words (HOWs) and oriented gradients (HOGs) to enable rich appearance representation of both structured (eg, cat face) and textured (eg, cat body) image regions. Learning the hierarchical model is a latent SVM problem which can be solved by the incremental concave-convex procedure (iCCCP). The resulting system is comparable with the stateof-the-art methods when evaluated on the challenging public PASCAL 2007 and 2009 datasets.