Human Action Recognition Based on Discriminative Sparse Representation on Multi-Manifolds
Publish place: Journal of Advances in Computer Research، Vol: 7، Issue: 1
Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
View: 522
This Paper With 14 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JACR-7-1_008
تاریخ نمایه سازی: 16 شهریور 1395
Abstract:
Human action recognition is an important problem in computer vision. One ofthe methods that are recently used is sparse coding. Conventional sparse codingalgorithms learn dictionaries and codes in an unsupervised manner and neglectclass information that is available in the training set. But in this paper for solvingthis problem, we use a discriminative sparse code based on multi-manifolds. Wedivide labeled data samples into multi-manifolds and also to decrease run timereduce dimension of manifolds. We find k inter nearest neighbors and intra nearestneighbors for each data sample in each manifold. The intra class variance should beminimized while the inter class variance should be maximized, in the result we couldcalculate laplacian matrix and optimize sparse code and dictionary. Then we usediscriminative sparse error for classification. We run this method on KTH and UCFsport datasets. Results show that we obtain a better result (about 89%) in UCFdataset.
Keywords:
Authors
Atefe Aghaei
University college of Rouzbahan, Sari ,Iran
Sajjad Tavassoli
Department of Computer Engineering, Sari branch, Islamic Azad University, Sari, Iran