A Novel Fusion Method for Semantic Concept Classification in Video

Abstract

Semantic concept classification is a critical task for content-based video retrieval. Traditional methods of machine learning focus on increasing the accuracy of classifiers or models, and face the problems of inducing new data errors and algorithm complexity. Recent researches show that fusion strategies of ensemble learning have appeared promising for improving the classification performance, so some researchers begin to focus on the ensemble of multi-classifiers. The most widely known method of ensemble learning is the Adaboost algorithm. However, when comes to the video data, it encounters severe difficulties, such as visual feature diversity, sparse concepts, etc. In this paper, we proposed a novel fusion method based on the CACE (Combined Adaboost Classifier Ensembles) algorithm. We categorize the visual features by different granularities and define a pair-wise feature diversity measurement, then we construct the simple classifiers based on the feature diversity, and use modified Adaboost to fusion the classifier results. The CACE algorithm in our method makes it outperform the standard Adaboost algorithm as well as many other fusion methods. Experimental results on TRECVID 2007 show that our method is an effective and relatively robust fusion method.