Towards a large-scale category detection with a distributed hierarchical compositional model

In this paper we evaluate a visual object detection system implemented on a distributed processing platform, presented in our previous work, with the goal of assessing the scalability of the system to a large-scale category detection. While state-of-the-art detection methods based on sliding windows may not be capable of scaling to a higher number of categories, we provide initial evidence that using a hierarchical compositional method called learned-hierarchy-of-parts (LHOP) may be capable of scaling to a higher number of categories. We show with the library trained on an MPEG-7 Shape database that the method is capable of scaling from a system with 5 categories and 6 second averaged response time to a system with 70 categories and averaged response time of 27 seconds.