Image Search and Retrieval Using Support Vector Machines
Lipo Wang
School of Electrical & Electronic Engineering
Institute for Media Innovation
Nanyang Technological University
Singapore 639798
Abstract
Image search and retrieval have become increasingly important due to abundance in multimedia
data. Relevance feedback (RF) is a powerful tool to bridge the gap between low-level visual
features and high-level semantic concepts during image retrieval. Although many algorithms have
obtained promising performance in various practical applications, content-based image retrieval
(CBIR) is still an open research topic mainly due to the difficulties in bridging the semantic gap.
We focus on developing and applying support vector machines (SVM) to maximize the potential
of CBIR with RF. Specially, we tackled the following problems:
(1) To incorporate the asymmetric property of training data with conventional SVM-based RF, a
biased maximum margin analysis (BMMA) method is designed based on the graph embedding
framework to separate the positive and negative feedback samples by a maximum margin in the
reduced subspace. By introducing a Laplacian regularizer to BMMA, semi-supervised BMMA
(SemiBMMA) is also proposed to utilize the information of unlabeled samples for SVM-based RF.
(2) To select the most informative samples for the user to label, a geometric optimum
experimental design (GOED) method is proposed to select multiple representative samples in the
database as the most informative ones.
(3) To exploit the RF log data, conjunctive patches subspace learning (CPSL) with side information
is developed. CPSL can directly learn a semantic concept subspace from the RF log data with a set
of similar and dissimilar pair-wise constraints without using any explicit class label information,
and this is more practical and useful in many real-world applications.
Experiments on a real-world image database have demonstrated that the proposed schemes can
significantly improve on existing approaches.