Clustering Guided SVM for Semantic Image Retrieval
SVM (support vector machine) enables effective image classification for semantic image retrieval. However, how to train accurate image classifiers in high-dimensional feature space suffers from the problem of choosing proper training samples. To solve this problem, a novel approach named CGSVM (clustering guided SVM) is presented, which utilizes clustering result to select the most informative image samples to be labeled, and optimize the penalty coefficient. Experimental results show that our algorithm achieves higher search accuracy than regular SVM for semantic image retrieval.