Attention-Driven Parts-Based Object Detection

Recent studies have argued that natural vision systems perform classification by utilizing different mechanisms depending on the visual input. In this paper we present a hybrid, data-driven object detection system that combines parts-based matching and view-based attention for faster detection. We propose a simple competitive policy that allows incremental addition of new object classes to the system without requiring class-vs-class training. Using our framework, we show empirical support for the hypothesis that low-frequency visual information can be effectively used to direct attention and possibly subsume further, more costly analysis. We evaluate our approach on face and car detection problems, while concentrating on the capability to learn from small samples. Our implementation is freely available as Matlab source code.

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Ilkka Autio, Jussi T. Lindgren
• Keywords: Artificial Intelligence, Car Detection Problems, Data-driven Object Detection, ECAI 2004, Faster Detection