Efficient Extraction of Feature Signatures Using Multi-GPU Architecture

Abstract

Recent popular applications like online video analysis or image exploration techniques utilizing content-based retrieval create a serious demand for fast and scalable feature extraction implementations. One of the promising content-based retrieval models is based on the feature signatures and the signature quadratic form distance. Although the model proved its competitiveness in terms of the effectiveness, the slow feature extraction comprising costly k-means clustering limits the model only for preprocessing steps. In this paper, we present a highly efficient multi-GPU implementation of the feature extraction process, reaching more than two orders of magnitude speedup with respect to classical CPU platform and the peak throughput that exceeds 8 thousand signatures per second. Such an implementation allows to extract requested batches of frames or images online without annoying delays. Moreover, besides online extraction tasks, our GPU implementation can be used also in a traditional preprocessing and training phase. For example, fast extraction allows indexing of huge databases or inspecting significantly larger parameter space when searching for an optimal similarity model configuration that is optimal according to both efficiency and effectiveness.