Pattern Classification Using Bag-of-Keypoints

The classifications when a target is not properly extracted due to improper segmentation include the multiclass case, in which the target contains objects belonging to different classes. In this paper, a method is applied to transform the multiclass case to a single-label classification by creating merged classes. To train merged classes, each feature must be defined in a very small domain, and the range of each feature must be binary, i.e., {0, 1}. It is not a contradiction to consider that the range of each feature is binary when the naïve Bayes classifier is employed in the bag-ofkeypoints method. Thus, a fuzzy extension technique is proposed that enables us to consider the range of each feature as continuous, i.e., [0, 1]. By using the weighted average operation of the fuzzy vector, the ordinary Bayes classifier can be applied to solve multiclass cases. The experimental results verify that the classifier correctly detects 1) multiclass targets, and 2) targets in the incomplete case, in which the target is not properly extracted. Keywords— template matching; multi-label classification; local feature; naïve Bayes classifier; feature selection; fuzzy vector; SVM