Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
CHARMS: a simple framework for adaptive simulation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
CLAM: an OO framework for developing audio and music applications
OOPSLA '02 Companion of the 17th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Minimizing development and maintenance costs in supporting persistently optimized BLAS
Software—Practice & Experience - Research Articles
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
Octave and Python: High-Level Scripting Languages Productivity and Performance Evaluation
HPCMP-UGC '06 Proceedings of the HPCMP Users Group Conference
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Hadoop: The Definitive Guide
The cactus framework and toolkit: design and applications
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
Opportunities and challenges of parallelizing speech recognition
HotPar'10 Proceedings of the 2nd USENIX conference on Hot topics in parallelism
Dialocalization: Acoustic speaker diarization and visual localization as joint optimization problem
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Processing web-scale multimedia data
Proceedings of the international conference on Multimedia
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A domain-specific approach to heterogeneous parallelism
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the second international workshop on MapReduce and its applications
Magellan: experiences from a science cloud
Proceedings of the 2nd international workshop on Scientific cloud computing
CUDA-level performance with python-level productivity for Gaussian mixture model applications
HotPar'11 Proceedings of the 3rd USENIX conference on Hot topic in parallelism
Analysis of Minimum Distances in High-Dimensional Musical Spaces
IEEE Transactions on Audio, Speech, and Language Processing
Audio Keywords Discovery for Text-Like Audio Content Analysis and Retrieval
IEEE Transactions on Multimedia
Speaker Diarization: A Review of Recent Research
IEEE Transactions on Audio, Speech, and Language Processing
There is no data like less data: percepts for video concept detection on consumer-produced media
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
Hi-index | 0.00 |
In this new era dominated by consumer-produced media there is a high demand for web-scalable solutions to multimedia content analysis. A compelling approach to making applications scalable is to explicitly map their computation onto parallel platforms. However, developing efficient parallel implementations and fully utilizing the available resources remains a challenge due to the increased code complexity, limited portability and required low-level knowledge of the underlying hardware. In this article, we present PyCASP, a Python-based framework that automatically maps computation onto parallel platforms from Python application code to a variety of parallel platforms. PyCASP is designed using a systematic, pattern-oriented approach to offer a single software development environment for multimedia content analysis applications. Using PyCASP, applications can be prototyped in a couple hundred lines of Python code and automatically scale to modern parallel processors. Applications written with PyCASP are portable to a variety of parallel platforms and efficiently scale from a single desktop Graphics Processing Unit (GPU) to an entire cluster with a small change to application code. To illustrate our approach, we present three multimedia content analysis applications that use our framework: a state-of-the-art speaker diarization application, a content-based music recommendation system based on the Million Song Dataset, and a video event detection system for consumer-produced videos. We show that across this wide range of applications, our approach achieves the goal of automatic portability and scalability while at the same time allowing easy prototyping in a high-level language and efficient performance of low-level optimized code.