Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Personalized multimedia retrieval: the new trend?
Proceedings of the international workshop on Workshop on multimedia information retrieval
Optical Flow Computation on Compute Unified Device Architecture
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Parallelization and Performance Analysis of Video Feature Extractions on Multi-Core Based Systems
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Browsing a Large Collection of Community Photos Based on Similarity on GPU
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Using graphics processors for high performance IR query processing
Proceedings of the 18th international conference on World wide web
Clustering billions of data points using GPUs
Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop
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Media mining, the extraction of meaningful knowledge from multimedia content, poses significant computational challenges in today's platforms, particularly in real-time scenarios. In this paper, we show how Graphic Processing Units (GPUs) can be leveraged for compute-intensive media mining applications. Furthermore, we propose a parallel implementation of color visual descriptors (color correlograms and color histograms) commonly used in multimedia content analysis on a CUDA (Compute Unified Device Architecture) enabled GPU (the Nvidia GeForce GTX280 GPU). Through the use of shared memory as software managed cache and efficient data partitioning, we reach computation throughputs of over 1.2 Giga Pixels/sec for HSV color histograms and over 100 Mega Pixels/sec for HSV color correlograms. We show that we can achieve better than real time performance and major speedups compared to high-end multicore CPUs and comparable performance on known implementations on the Cell B.E. We also study different trade-offs on the size and complexity of the features and their effect on performance.