| Summary: | 碩士 === 中華大學 === 資訊管理學系 === 94 === Content-based image retrieval has become an important topic in image processing and computer vision. In this paper, we used differ features such as moment invariant, centroid Fourier descriptor, and complex Fourier descriptor in Support Vector Machine for the shape-based image retrieval. The result shows that the best feature is the complex Fourier descriptor. Our approach is first to obtain the features. Secondly, the features and the class label are associated to form the training data and fed to the Support Vector Machine to get the training model. Then, the testing shapes are fed to the model to find the predicted class. Finally, we select the best matched image by computing the least mean square error distance among the images of the predicted class. We also demonstrate a way to track the pose of the testing image.
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