Automatic image region annotation through segmentation based visual semantic analysis and discriminative classification
Jing Zhang, Yongwei Gao, Shengwei Feng, Yubo Yuan, Chin‐Hui Lee · 2016
We propose a new framework for automatic image annotation (AIA) of regions through segmentation based semantic analysis and discriminative classification. Given a test image, it is first segmented by a proposed texture-enhanced JSEG algorithm. Then these regions are represented by an extended bag-of-words model in which a feature vector, based on a visual lexicon with its vocabulary consisting of a visual word or a co-occurrence of multiple visual words, is constructed to represent the region content. Finally a concept classifier learned by a maximal figure-of-merit algorithm is used to predict the region labels. These models are discriminatively trained from image regions with multiple associations between regions and concepts. Experiments on a subset of the Corel 5K data set illustrate that our proposed approach to region AIA achieves more accurate annotation results than some sate-of-the-art algorithms.