Paper Info
Title
Novel Parallel Algorithm For Object Recognition With The Ensemble Of Classifiers Based On The Higher-Order Singular Value Decomposition Of Prototype Pattern Tensors
Abstract
In this paper a novel parallel algorithm for the tensor based classifiers for object recognition in digital images is presented. Classification is performed with an ensemble of base classifiers, each operating in the orthogonal subspaces obtained with the Higher-Order Singular Value Decomposition (HOSVD) of the prototype pattern tensors. Parallelism of the system is realized through the functional and data decompositions on different levels of computations. First, the parallel implementation of the HOSVD is presented. Then, the second level of parallelism is gained by partitioning the input dataset. Each of the partitions is used to train a separate tensor classifiers of the ensemble. Despite the computational speed-up and lower memory requirements, also accuracy of the ensemble showed to be higher compared to a single classifier. The method was tested in the context of object recognition in computer vision. The experiments show high accuracy and accelerated performance both in the training and classification stages.
Year
Venue
Keywords
2014
PROCEEDINGS OF THE 2014 9TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, THEORY AND APPLICATIONS (VISAPP 2014), VOL 2
Object Recognition, Tensor Processing, Higher-Order Singular Value Decomposition, Parallel Algorithms
Field
DocType
Citations 
Singular value decomposition,Pattern recognition,Random subspace method,Computer science,Parallel algorithm,Matrix decomposition,Prototype pattern,Artificial intelligence,Higher-order singular value decomposition,Classifier (linguistics),Cognitive neuroscience of visual object recognition
Conference
1
PageRank 
References 
Authors
0.35
0
2
Name
Order
Citations
PageRank
Boguslaw Cyganek114524.53
Katarzyna Socha210.35