Performance analysis of Bag-of-Features based content identification systems

Many state-of-the-art methods in image retrieval, classification and copy detection are based on the Bag-of-Features (BOF) framework. However, the performance of these systems is mostly experimentally evaluated and little results are reported on theoretical performance. In this paper, we present a statistical framework that makes it possible to analyse the performance of a simple BOF-system and to better understand the impact of different design elements such as the robustness of descriptors, the accuracy of encoding/assignment, information preserving pooling and finally decision making. The proposed framework can be also of interest for a security and privacy analysis of BOF systems.