Selection of effective training instances for scalable automatic image annotation

Abstract

Automatic image annotation means employing learning models for describing visual contents of images by using text descriptors. With the fast growth of digital images in the web, large-scale automatic image annotation has started to deal with major challenges. The most important challenges are scalability and annotation performance. In this research, in order to solve scalability and the image annotation time challenge, the prototype selection approach is used. The assumption of the prototype selection is based on single-label instances while, in image annotation, an instance has more than one label. It means that instances are multi-label. Hence, to employ prototype selection algorithms in image annotation, focusing on the concept of multi-label is a critical task. Thus, taking an appropriate measure in these methods to compute the rate of dissimilarity between label vectors has a great importance. The proposed approach in this paper is based on multi-labeling of prototype selection methods by selecting a modifying appropriate binary dissimilarity measure, in comparison two label vectors. The effectiveness of the proposed approach in reducing the number of training instances and selecting effective ones has been shown by experiments on large-scale NUS-WIDE family image sets. The experimental results showed the effectiveness of the proposed approach in reducing the number of instances and improving annotation performance.