On approaching 2D-FPCA technique to improve image representation in frequency domain
| Autor: | Thai Hoang Le, Duc Minh Vo, Ha Thi Thanh Do, Hung Phuoc Truong |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Mathematical optimization
business.industry Computer science Covariance matrix Stability (learning theory) Pattern recognition computer.software_genre Information extraction Robustness (computer science) Frequency domain Principal component analysis Artificial intelligence Representation (mathematics) business computer Subspace topology |
| Zdroj: | SoICT |
| DOI: | 10.1145/2542050.2542061 |
| Popis: | A novel approach based on structure information extraction in frequency domain is proposed for image representation problem. Regarding this problem, a new subspace method based on Two-dimensional Fractional Principle Component Analysis (2D-FPCA) in frequency domain is applied to images, thus extracting the texture information. In order to extract the structure information, the system utilizes this new subspace as the bilateral consideration of 2D-FPCA technique called B2D-FPCA. For this purpose: (1) we first introduce the theory of 2D-FPCA based on the definition of fractional variance and fractional covariance matrix; (2) then show its improvement called Bilateral 2D-FPCA and (3) the robustness of 2D-DCT is also described as the preprocessing step. This approach is applied to facial expression representation problem to prove the stability and robustness of the proposed framework. For demonstration, facial expressions datasets (JAFFE, Pain expression subset and Cohn-Kanade) are used in order to compare the proposed framework with some other approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|