Learning a Maximum Margin Subspace for Image Retrieval

摘要

One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap been low level visual features and high level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user provided information, a classifier can be learned to discriminate between positive and negative examples. However, in real world applications, the number of user feedbacks is usually too small comparing to the dimensionality of the image space. Thus, a situation of overfitting may occur. In order to cope with the high dimensionality, we propose a novel supervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims to maximize the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the global Euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image, thus the retrieval performance can be enhanced. The experimental results on a large image database demonstrates the effectiveness and efficiency of our proposed algorithm.