Automation of image categorization with most relevant negatives

Abstract

Image categorization requires the algorithm to be learned in order to obtain the efficient categorization. The algorithm used for image categorization may misclassify images that are visually similar to the positive ones. Generally, sampling negatives is done at random. In this paper, we have improved Negative Bootstrap in an efficient way to obtain most relevant negatives. To obtain most misclassified visually similar images in a faster way, fast intersection kernel SVM is generalized and used for classification. The accuracy of classified visual concepts is obtained by using the performance metrics. Several different metrics have been used to show the accuracy of relevant negatives. Manual labeling of negatives could be avoided by using the efficient negative bootstrap algorithm.