Video shot retrieval using a kernel derived from a continuous HMM

In this paper, we propose a discriminative approach for retrieval of video shots characterized by a sequential structure. The task of retrieving shots similar in content to a few positive example shots is more close to a binary classification problem. Hence, this task can be solved by a discriminative learning approach. For a content-based retrieval task the twin characteristics of rare positive example occurrence and a sequential structure in the positive examples make it attractive for us to use a learning approach based on a generative model like HMM. To make use of the positive aspects of both discriminative and generative models, we derive Fisher and Modified score kernels for a Continuous HMM and incorporate them into SVM classification framework. The training set video shots are used to learn SVM classifier. A test set video shot is ranked based on its proximity to the positive class side of hyperplane. We evaluate the performance of the derived kernels by retrieving video shots of airplane takeoff. The retrieval performance using the derived kernels is found to be much better compared to linear and RBF kernels.