Colour image retrieval based on mean vector and covariance tests
| Autor: | B. Sathiyaprasad, K. Seetharaman |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation Covariance 01 natural sciences 010104 statistics & probability Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Test statistic Preprocessor 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business Image retrieval Statistical hypothesis testing |
| Zdroj: | 2017 International Conference on Intelligent Sustainable Systems (ICISS). |
| DOI: | 10.1109/iss1.2017.8389243 |
| Popis: | In this paper, statistical test of hypothesis based method is proposed, which retrieves manifold color images such as structured, semi-structured, textured, scaled, and rotated. The proposed method, first, performs preprocessing and then employs the test statistic: test for equality of covariance, and generalized component test. If the query and target images pass the covariance test, then it proceeds to test the mean vectors of the two images; otherwise, the test is dropped. If the query and target images pass both tests, then it is concluded that the images belong to same class. Otherwise, they belong to different class. The performance of the proposed method is evaluated using the image database and its feature database. The images have been collected from various datasets to maintain heterogeneity of the images. The proposed method yields better results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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