Fundamentals of image data mining: analysis, features, classification and retrieval

摘要

Data mining is "the science of extracting useful knowledge from huge data repositories" [1]. Extending this definition, it can be concluded that image data mining is the science of extracting valuable knowledge from large volumes of images. The process of mining image data is theoretically simple. The first step is to extract features or properties from the source image, for example, water, trees, airplanes, clouds, cars, and so on. The second step is to use these features to classify the image within some class (also known as image classification) or to search in a database for similar images (better known as image retrieval). Image data mining is used in various fields: military reconnaissance, weather forecasting, agriculture, and even medicine, where medical images can be used in disease diagnoses. This book presents the latest developments in the field of image data mining and is divided into four parts: "Preliminaries" (three chapters), "Image Representation and Feature Extraction" (three chapters), "Image Classification and Annotation" (four chapters), and "Image Retrieval and Presentation" (three chapters).