A novel approach for image feature extraction using HSV model color and niters wavelets
López Del Alamo, Cristian
Fuentes Pérez, Lizeth Joseline
Romero Calla, Luciano Arnaldo
Ramos Lovón, Wilber Roberto
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Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the HSV color model with Gabor wavelets.