期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：3
页码：5473
DOI：10.15680/IJIRCCE.2017.0503336
出版社：S&S Publications
摘要：The image mining technique deals with the extraction of implicit knowledge and image with datarelationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain.Textures are one of the basic features in visual searching, computational vision and also a general property of anysurface having ambiguity. The main objective of this paper is to apply image mining in the domain such as differentleaves belonging to different trees to classify and detect the exact belonging class of tree. Leaf images of different treeswith different texture and shape including sizes can be classified into many classes as per given data base and toexplore the feasibility of data mining approach. Results will show that there is promise in image mining based oncontent. It is well known that data mining techniques are more suitable to larger databases than the one used for thesepreliminary tests. In particular, a Computer aided method based on association rules becomes more accurate with alarger dataset. Traditional association rule algorithms adopt an iterative method to discovery frequent item set, whichrequires very large calculations and a complicated transaction process. Because of this, a new association rulealgorithm is proposed in this paper. Experimental results show that this new method can quickly discover frequent itemsets and effectively mine potential association rules. A total of 26 features including histogram intensity features andGLCM features are extracted from leaf images. Experiments have been taken for a data set of 322 images taken ofdifferent types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns. Theaccuracy obtained by this method is approximately 97% which is highly encouraging.
