Image Retrieval System and Rough Set Theory
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Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules a...
Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. This book presents an improved approach to select significant features from the huge image feature vector. The concept behind this research is that it is possible to extract image feature relational patterns in an image feature vector database. After which, these relational patterns are used to generate rules and improve the retrieval results for a CBIR system. In addition, this research proposes a CBIR system utilising the Rough Set instead of deterministic and crisp methods. In this research, Rough Set rules are evaluated with noisy images. Also, in order to have a more accurate classifier in the CBIR system, the classifier is proposed to be based on the Rough Set and Support Vector Machine (SVM) in this research.
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