ISSN: 2250-3005 - ijcer

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International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Image Mining Method and Frameworks1 Shaikh Nikhat FatmaDepartment Of Computer, Mumbai University, Pillai’s Institute Of Information Technology,New PanvelAbstract:Image mining deals with the extraction of image patterns from a large collection of images. Clearly, image miningis different from low-level computer vision and image processing techniques because the focus of image mining is inextraction of patterns from large collection of images, whereas the focus of computer vision and image processingtechniques is in understanding and / or extracting specific features from a single image. While there seems to be someoverlaps between image mining and content-based retrieval (both are dealing with large collection of images), image mininggoes beyond the problem of retrieving relevant images. In image mining, the goal is the discovery of image patterns that aresignificant in a given collection of images.Keywords— Image mining (IM); function-driven; knowledge driven; information driven; knowledge remountingI. INTRODUCTIONImage mining deals with extraction of implicit knowledge, image data relationship or other patterns not explicitlystored in images and uses ideas from computer vision, image processing, image retrieval, data mining, machine learning,databases and AI. The fundamental challenge in image mining is to determine how low-level, pixel representation containedin an image or an image sequence can be effectively and efficiently processed to identify high-level spatial objects andrelationships. Typical image mining process involves pre-processing, transformations and feature extraction, mining (todiscover significant patterns out of extracted features), evaluation and interpretation and obtaining the final knowledge.Various techniques from existing domains are also applied to image mining and include object recognition, learning,clustering and classification, just to name a few. Association rule mining is a well-known data mining technique that aims tofind interesting patterns in very large databases. Some preliminary work has been done to apply association rule mining onsets of images to find interesting patterns[4,5,7]. The fundamental challenge in image mining is to determine how low-level,pixel representation contained in a raw image or image sequence can be efficiently and effectively processed to identifyhigh-level spatial objects and relationships. In other words, image mining deals with the extraction of implicit knowledge,image data relationship, or other patterns not explicitly stored in the image databases.Research in image mining can be broadly classified into two main directions. The first direction involves domain-specificapplications where the focus is in the process of extracting the most relevant image features into a form suitable for datamining. The second direction involves general applications where the focus is on the process of generating image patternsthat maybe helpful in the understanding of the interaction between high-level human perceptions of images and low levelimage features. The latter may lead to improvements in the accuracy of images retrieved from image databases.In the remaining paper in section II there is an explanation for the image mining process, in section III we take thereview of how Image Mining is actually done actually work. In section IV we take an example. In section V we take anover view of Image Mining Frameworks. In section VI we make a conclusion for Image Mining and the last sectionincludes all references for this paper.II. THE IMAGE MINING PROCESSFigure 1 shows the image mining process. The images from an image database are first preprocessed to improvetheir quality. These images then undergo various transformations and feature extraction to generate the important featuresfrom the images. With the generated features, mining can be carried out using data mining techniques to discover significantpatterns. The resulting patterns are evaluated and interpreted to obtain the final knowledge, which can be applied toapplications.||Issn 2250-3005(online)|| ||December||2012|| Page 135
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8Figure 1: The image mining processIt should be noted that image mining is not simply an application of existing data mining techniques to the imagedomain. This is because there are important differences between relational databases versus image databases:(a) Absolute versus relative valuesIn relational databases, the data values are semantically meaningful. For example, age is 35 is well understood. However, inimage databases, the data values themselves may not be significant unless the context supports them. For example, a greyscale value of 46 could appear darker than a grey scale value of 87 if the surrounding context pixels values are all verybright.(b) Spatial information (Independent versus dependent position)Another important difference between relational databases and image databases is that the implicit spatial information iscritical for interpretation of image contents but there is no such requirement in relational databases. As a result, imageminers try to overcome this problem by extracting position-independent features from images first before attempting to mineuseful patterns from the images.(c) Unique versus multiple interpretationA third important difference deals with image characteristics of having multiple interpretations for the same visual patterns.The traditional data mining algorithm of associating a pattern to a class (interpretation) will not work well here. A new classof discovery algorithms is needed to cater to the special needs in mining useful patterns from images.III. METHOD FOR IMAGE MININGIn this section, we present the algorithms needed to perform the mining of associations within the context of images. Thefour major image mining steps are as follows:1. Feature extraction. Segment images into regions identifyable by region descriptors (blobs). Ideallyone blob represents one object. This step is also called segmentation.2. Object identification and record creation. Compare objects in one image to objects in every other image. Label eachobject with an id. We call this step the preprocessing algorithm.3. Create auxiliary images. Generate images with identified objects to interpret the association rules obtained from thefollowing step.4. Apply data mining algorithm to produce object association rules.The idea of this method is selecting a collection of images that belong to a specific field (e.g. weather), after the selectionstage we will extract the objects from each image and indexing all the images with its objects in transaction database, thedata base contain image identification and the objects that belong to each images with its features. After creating thetransaction data base that contains all images and its feature we will use the proposed data mining methods to associate rulesbetween the objects. This will help us for prediction (e.g. if image sky contain black clouds then it will rain (65%))[5].The following block diagram presents the proposed IM method:||Issn 2250-3005(online)|| ||December||2012|| Page 136
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ISSN: 2250-3005 - ijcer

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