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THE SURFACE CURVEDNESS OPERATOR AND ITS APPLICATION TO IMAGE CLASIFICATION AND IMAGE RETRIEVAL

 

TITLE THE SURFACE CURVEDNESS OPERATOR AND ITS APPLICATION TO IMAGE CLASIFICATION AND IMAGE RETRIEVAL.
AUTHOR TANONGCHAI SOOKAWAT
DEGREE MASTER OF SCIENCE PROGRAMME IN COMPUTER SCIENCE
FACULTY FACULTY OF SCIENCE
ADVISOR SUKANYA PHONGSUPHAP
CO-ADVISOR DAMRAS WONGSAWANG
 
ABSTRACT
This research proposed a method to define a feature by using the Surface Curvedness Operator or SC-operator. This operator can be used to describe the altitude of surface curvature by using the concept of topographic structure, which can be used to view pixel-based image as 3D surface for use in conjunction with features derived from the Surface-Shape operator for image classification and image retrieval applications. The SC-operator is related to the curvedness of the image surface. The SC-operator was defined by using the relation of two eigenvalues of the Hessian Matrix, which is the distance of the coordinate of eigenvalues from origin on the eigenvalue plane. With the SC-operator, the feature called the Roughness Index was derived to use together with the clumpiness and miscibility values, which are derived from the Surface Shape Operator and used to describe the shape of image surfaces. The Fractal Theory and the Scale Space Theory were used to develop the roughness index in a multiscale approach. Euclidean distance and Mahalanobis distance were used as classifiers and similarity measures. In our experiments, the clumpiness and miscibility were calculated from 4 different scales. While the roughness index was calculated from 5 different scales. Images used in the experiments were obtained from the Brodatz Texture Image Database, which consists of 112 categories. For each category, 18 images were selected, ten of which were used as a training set and eight others as a testing set for image classification. For image retrieval, 896 (112x8) images were used, as the testing database. Results showed that the roughness index could be used to enhance the results of image classification and image retrieval applications. For image classification, the percentage of correct classification was improved from 86.38% to 87.95%. For the image retrieval, the roughness index could be also used to enhance the accuracy rate from 77% to 80% when considering the top 8 retrieved images and 96% to 98% when considering the top 100 retrieved images. In addition, it could be concluded that Mahalanobis distance provides better results than Euclidean distance.
KEYWORD COMPUTER VISION / ROUGHNESS INDEX / MULTISCALE ANALYSIS / IMAGE CLASSIFICATION / IMAGE RETRIEVAL

 

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