AI Based Feature Extraction Through Content Based Image Retrieval

2020 ◽  
Vol 17 (9) ◽  
pp. 4050-4054
Author(s):  
C. Gururaj ◽  
Satish Tunga

Therapeutic pictures are progressively being utilized inside human services for conclusion, arranging treatment, controlling treatment and checking sickness movement. In reality, helpful imaging prevalently shapes vague, missing, dubious, essential, clashing, dull restricting, contorted data additionally, information has a strong fundamental character. The proposed approach can be used to achieve the accuracy by using the artificial intelligence techniques wherein the disease level is identified by comparing it with the artificial intelligence data. The two fold merit of this system is it provides better accuracy and also determines all the possibilities of spreading of the disease including the various stages of the disease. This research work also represents new automated strategies of the division and arrangement of therapeutic pictures utilizing computerized reasoning, i.e., delicate processing strategies, data combination and particular area information. Promising outcomes demonstrate the predominance of the delicate processing and information based approach over best customary systems as far as division mistakes. The arrangement of various structures is made by executing rules obtained by both space literature and by medical experts.

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.


Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques


2018 ◽  
pp. 1455-1459
Author(s):  
Raimondo Schettini ◽  
Gianluigi Ciocca ◽  
Isabella Gagliardi

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