2026-09-21T04:53:02Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/3770 2021-12-13T03:56:24Z 1582963302567:1597824273898 user-ucsy

Image Database Classification and Retrieval Using Support Vector Machines (SVM) Thant, Wut Yee Khaing, Thiri Thitsar In this paper, we propose a support vector machines (SVMs) method of classifying image regions hierarchically based on their semantics rather than on low-level features. First, image regions are segmented using the hill-climbing method. And then, the support vector machines classify these regions. The SVMs learn the semantics of specified classes from a database of image regions. A support vector machine was used as the classifier. We developed a new way to assign probability after multi-class SVM classification. Our approach achieved approximately 90% accuracy on a collection of images with minimal noise. A support vector machine (SVM) is used to classify the feature vectors. To reduce the computation time and improve the classification accuracy. We also developed a new way to compute probabilistic outputs from a multi-class support vector machines. 2009-12-30 http://hdl.handle.net/20.500.12678/0000003770 https://meral.edu.mm/records/3770