FABC: retinal vessel segmentation using adaboost
IEEE Transactions on Information Technology in Biomedicine
A Chinese web page automatic classification system
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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This paper presents a comparative study on five feature selection heuristics applied to a retinal image database called DRIVE. Features are chosen from a feature vector (encoding local information, but as well information from structures and shapes available in the image) constructed for each pixel in the field of view (FOV) of the image. After selecting the most discriminatory features, an AdaBoost classifier is applied for training. The results of classifications are used to compare the effectiveness of the five feature selection methods.