Biased Support Vector Machine for Relevance Feedback in Image Retrieval

• Main Authors: HOI, Steven, CHAN, Chi-Hang, HUANG, Kaizhu, LYU, Michael R., KING, Irwin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2004
• Subjects: content-based retrieval; image classification; image retrieval; relevance feedback; support vector machines; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2399; https://ink.library.smu.edu.sg/context/sis_research/article/3399/viewcontent/BiasedSupportVectorMachineRefFeedbackIR_2004.pdf
• Institution: Singapore Management University

Recently, support vector machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not consider an important issue of relevance feedback, i.e. the unbalanced dataset problem, in which the negative instances largely outnumber the positive instances. For solving this problem, we propose a novel technique to formulate the relevance feedback based on a modified SVM called biased support vector machine (Biased SVM or BSVM). Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate the performance of our algorithms, in which promising results demonstrate the effectiveness of our techniques.