Journals Proceedings

International Journal of Advances in Computer Science and Its Applications

Image Mining Based on Concept Lattice Theory Using Texture Feature



An important part of our knowledge is in the form of images. Discovering knowledge from data stored in typical alphanumeric databases, such as relational databases, has been the focal point of most of the work in database mining. However, with advances in secondary and tertiary storage capacity more and more non standard data (in the form of images) is being accumulated. This vast collection of image data can also be mined to discover new and valuable knowledge. During the process of image mining, the concepts in different hierarchies and their relationships are extracted from different hierarchies and granularities, and association rule mining and concept clustering are consequently implemented. The generalization and specialization of concepts are realized in different hierarchies, lower layer concepts can be upgraded to upper layer concepts, and upper layer concepts guide the extraction of lower layer concepts. It is a process from image data to image information, from image information to image knowledge, from lower layer concepts to upper layer concepts. In this paper framework of image mining based on concept lattice is proposed. The methods of image mining from image texture features are introduced here, which include the following basic steps: firstly pre-process images secondly use cloud model to extract concepts, lastly use concept lattice to extract a series of image knowledge.

No fo Author(s) : 2
Page(s) : 373 - 378
Electronic ISSN : 2250 - 3765
Volume 1 : Issue 1
Views : 479   |   Download(s) : 170