Dimensionality reduction of SIFT using PCA for object categorization
| Autor: | Sanparith Marukatat, Supavadee Aramvith, Supakorn Siddhichai, Nattachai Watcharapinchai |
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| Rok vydání: | 2009 |
| Předmět: |
Computer science
business.industry Dimensionality reduction Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-invariant feature transform Pattern recognition Object detection Categorization Feature (computer vision) Principal component analysis Computer vision Artificial intelligence business Feature detection (computer vision) |
| Zdroj: | 2008 International Symposium on Intelligent Signal Processing and Communications Systems. |
| DOI: | 10.1109/ispacs.2009.4806729 |
| Popis: | The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the Scale Invariant Feature Transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-Nearest Neighbour). In this paper, we propose the dimensionality reduction of SIFT using Principal Component Analysis (PCA) on each object category to reduce computational complexity and memory requirement during training process. Experimental results show that our proposed technique can reduce the dimension of SIFT up to around 80% with the same average precision compared to baseline technique without our proposed method. |
| Databáze: | OpenAIRE |
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