M-VCR: Multi-View Consensus Recognition for Real-Time Experimentation
David Wesley McKee, Paul Townend, David Webster, Jie Xu · 2014
A major application area in the computer vision domain is gesture recognition, requiring real-time image classification to respond to human interactions. However, current state-of-the-art high-quality algorithms for image classification do not meet many dynamic real-time requirements. This paper presents the development of M-VCR - a novel approach for improving the reliability of real-time image classification. M-VCR increases the quality of classifications under real-time constraints through the adoption of fast classification algorithms, although these algorithms individually produce lower quality results, utilisation under a 'consensus' approach can achieve results equivalent to those of much higher-quality algorithms. The proposed approach also allows for different algorithms to be utilised in parallel, building on the fault tolerance technique of N-versioning. A significant improvement in image classification is experimentally demonstrated for both the SURF and MSER feature detectors through our integration consensus approach. This improvement is delivered entirely through the integration method without requiring modification of the source algorithms being used.