While the two fields do share a large number of common functions such as feature extraction, they differ in their fundamental assumptions. Perhaps the most common misconception of image mining is that image mining is yet another term for pattern recognition. In image mining, the goal is the discovery of image patterns that are significant in a given collection of images and the related alphanumeric data. While there seems to be some overlap between image mining and content-based retrieval (since both deals with large collection of images), image mining goes beyond the problem of retrieving relevant images. ![]() ![]() The focus of image mining is in the extraction of patterns from a large collection of images, whereas the focus of computer vision and image processing techniques is in understanding and/or extracting specific features from a single image. Clearly, image mining is different from low-level computer vision and image processing techniques. These images, once mined, may reveal interesting patterns that could shed some lights on the behavior of the people living at that period of time. For example, in the field of archaeology, many photographs of various archeological sites have been captured and stored as digital images. While some of individual fields in themselves may be quite matured, image mining, to date, is just a growing research focus and is still at an experimental stage.īroadly speaking, image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images and between image and other alphanumeric data. It is an interdisciplinary endeavor that draws upon computer vision, image processing, image retrieval, machine learning, artificial intelligence, database and data mining, etc. Image mining is more than just an extension of data mining to image domain. ![]() Image mining can automatically discover these implicit information and patterns from the high volume of images and is rapidly gaining attention in the field of data mining. These images involve a great number of useful and implicit information that is difficult for users to discover. A vast amount of image data is generated in our daily life and each field, such as medical image (CT images, ECT images and MR images etc), satellite images and all kinds of digital photographs. Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases.
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