Image Search Reranking with Semi-supervised LPP and Ranking SVM

Abstract

Learning to rank is one of the most popular ranking methods used in image retrieval and search reranking. However, the high-dimension of the visual features usually causes the problem of “curse of dimensionality”. Dimensionality reduction is one of the key steps to overcome these problems. However, existing dimensionality reduction methods are typically designed for classification, but not for ranking tasks. Since they do not utilize ranking information such as relevance degree labels, direct utilization of conventional dimensionality reduction methods in ranking applications generally cannot achieve the best performance. In this paper, we study the task of image search reranking, and propose a novel system scheme based on Locality Preserving Projections (LPP) and RankingSVM. And further, in the proposed scheme, we improve LPP by incorporating the relevance degree information into it. Since this kind of method can use the information of labeled and unlabeled data, we name it as semi-supervised LPP (Semi-LPP). Experiments on the popular MSRA-MM dataset demonstrate the superiority of the proposed scheme and Semi-LPP method in image search reranking application.