ISSN: 2250-3005 - ijcer

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International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Figure 2: Block diagram of image mining method(1) Select a collection of images that belong to the samefield (E.g. medical images, geographical images, personsimages, etc.)(2) Image Retrieval. Image mining requires that images can be retrieved according to some requirement specifications. Inthe proposed work we comprise image retrieval by derived or logical features like objects of a given type or individualobjects or persons using edge detection techniques[8].After we extract object we will encoded it as follows:O1: circle.O2: triangle.O3: square.Figure 3: Example of an imageFigure 4: Object extraction using edge detection||Issn 2250-3005(online)|| ||December||2012|| Page 137
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8(3) Image Indexing. Image mining systems require a fast and efficient mechanism for the retrieval of image data.Conventional database systems such as relational databases facilitate indexing on primary or secondary key(s). We willcreate two databases:The first one contains all the objects that have been extracting from the images and its features[5].Table 1: First database contains the objects and its featuresTherefore the association rule with spatial relationships could be:V-Next-to ([red, circle, small], [blue, square, *]) ^ H-Next-to ([red, circle, *], [yellow, *, large]) Overlap([red,circle, *], [green, *, *]) (34%).In this example, only three dimensions were needed and we made use of the wildcard * to replace absent values.The second Database contains all the images and the objects that belong to each image.Table 2: Second Database Contains Each Image and its Objects(4) Finally, the last step is applying the proposed mining techniques using the data of the images that has been index tothe database.(5) After that we will use the first a proposed algorithm to find the frequent item sets from the specific table and the resultwill be the following:(6) The final step we will use the second proposed algorithm to find association rules between the objects and we will havethe following results:(1) {O4,O4} {O2,O2} [100%](2) {O2,O4,O4} {O2} [100%](3) {O3,O4} {O2} [100%](4) {O3} {O2,O4} [100%](5) {O2,O2} {O4} [100%](6) {O4,O4} {O2} [100%](7) {O3} {O2} [100%](8) {O3} {O4} [100%]IV. EXAMPLEA simple example illustrating how image mining algorithms work with n=10. The original images and theircorresponding blobs are shown on Figure 4 and Figure 5. Association rules corresponding to the identified objects are alsoshown . 10 representative images are chosen from the image set created for the experiments.||Issn 2250-3005(online)|| ||December||2012|| Page 138
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ISSN: 2250-3005 - ijcer

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