Hierarchical Implicit Shape Modeling

• We introduce hierarchical Implicit Shape Model (ISM).
• In hierarchical ISM, each object is modeled by a hierarchical star graph.
• It extracts a set of discriminative parts for each object category.
• The proposed approach does not require a verification stage.
• Our approach has superior performance on some known semi-rigid object datasets.

In this paper, a new hierarchical approach for object detection is proposed. Object detection methods based on Implicit Shape Model (ISM) efficiently handle deformable objects, occlusions and clutters. The structure of each object in ISM is defined by a spring like graph. We introduce hierarchical ISM in which structure of each object is defined by a hierarchical star graph. Hierarchical ISM has two layers. In the first layer, a set of local ISMs are used to model object parts. In the second layer, structure of parts with respect to the object center is modeled by global ISM. In the proposed approach, the obtained parts for each object category have high discriminative ability. Therefore, our approach does not require a verification stage. We applied the proposed approach to some datasets and compared the performance of our algorithm to comparable methods. The results show that our method has a superior performance.