Large-Scale Semantic Concept Detection on Manycore Platforms for Multimedia Mining

Abstract

Media mining, the extraction of meaningful knowledge from multimedia content has become a major application and poses significant computational challenges in today's platforms. Media mining applications contain many sophisticated algorithms that include data-intensive analysis, classification, and learning. This paper explores the use of Graphics Processing Units (GPU) in media mining. We are particularly focused on large-scale semantic concept detection, a state-of-the-art approach that maps media content to hight-level semantic concepts, and a building block in many Media mining applications. We present a fast, parallel, large-scale, high-level semantic concept detector that leverages the GPU for image/video retrieval and content analysis. Through efficient data partitioning and movement, we parallelize feature extraction routines. By interleaving feature extraction routines of different types, we increase the computational intensity and mitigate the negative effects of histogram-like reduction operations. To cope with the very large number of semantic concepts, we propose a data layout of concept models on a multi-GPU hybrid architecture for high throughput semantic concept detection. We achieve one to two orders of magnitude speedups compared to serial implementations and our experiments show that we can detect 374 semantic concepts at a rate of over 100 frames/sec. This is over 100 times faster than a LibSVM-based semantic concept detection.