Biased Support Vector Machine for Relevance Feedback in Image Retrieval

Biased Support Vector Machine for Relevance Feedback in Image Retrieval

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 feedba...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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