Improving semantic concept detection through optimizing ranking function
In this paper, a kernel-based learning algorithm, KernelRank, is presented for improving the performance of semantic concept detection. By designing a classifier optimizing the receiver operating characteristic (ROC) curve using KernelRank, we provide a generic framework to optimize any differentiable ranking function using effective smoothing functions. KernelRank directly maximizes a 1-D quality measure of ROC, i.e., AUC (Area under the ROC). It exploits the kernel density estimation to model the ranking score distributions and approximate the correct ranking count. The ranking metric is then derived and the learnable parameters are naturally embedded. To address the issues of computation and memory in learning, an efficient implementation is developed based on the gradient descent algorithm. We apply KernelRank with two types of kernel density functions to train the linear discriminant function and the Gaussian mixture model classifiers. From our experiments carried out on the development set for TREC Video Retrieval 2005, we conclude that 1) KernelRank is capable of training any differentiable classifier with various kernels; and 2) the learned ranking function performs better than traditional maximization likelihood or classification error minimization based algorithms in terms of AUC and average precision (AP).
semantic concept detection area under roc information retrieval multimedia database roc curve