(un)reliability of video concept detection
Great effort has been made to improve video concept detection and continuous progress has been reported. With the current evaluation method being confined to carefully annotated domains and thus quite forgiving, the reliability of the state-of-the-art concept classifiers remains in question. Adopting a more rigorous evaluation approach, we find that most concept classifiers built using the mainstream approach are unreliable because they generalize poorly to domains other than their training domain. Moreover, evidences show that SVM-based concept classifiers learn little beyond memorizing most of the positive training data, and behave close to memory-based models such as kNN indicated by comparable performance between the two models. Examining the properties of the reliable concept classifiers, we find that the classifiers of frequent concepts, "bloated" classifiers, and classifiers capable of learning the pattern of data, tend to be more reliable. This paper contributes to a better understanding of concept detection, suggests heuristics to identify reliable concept classifiers, and discusses solutions to improving concept detection reliability.
video concept detection knn generalizability svms