A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
The authors present a new framework for multimedia content analysis and retrieval, which consists of two independent algorithms. First, they propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, they propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation.