Radiological image retrieval technique using multi-resolution texture and shape features

Author(s):  
Sumit Kumar ◽  
Jitesh Pradhan ◽  
Arup Kumar Pal ◽  
SK Hafizul Islam ◽  
Muhammad Khurram Khan
2020 ◽  
Vol 17 (4) ◽  
pp. 1885-1888
Author(s):  
M. A. Muthiah ◽  
N. Mathan ◽  
E. Logashanmugam

Due to vast enhancement in the field of visual technology, there are various sets of images. In order to reduce the complexity in retrieval of relevant images CBIR (Content Based Image Retrieval) technique can be used. CBIR using only color feature does not result in required output. So in this paper we introduced the concept of hybrid model which deals with color, texture along with shape features which gives an efficient output. A set of images are used to test the accuracy and the precision of each methods. Using Euclidean distance and Manhattan distance, similarity between query image and all the other images in database are calculated. Then the calculated distance values are arranged in ascending order. Based on this required images are retrieved. Experiment results shows that Hybrid model method had high accuracy and precise output compared to Color Histogram. Future work will be made to add one more feature (shape features) in order to get better results.


2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  

Author(s):  
Dr. S. Thavamani ◽  

Duplicated images cause several problems in online sites, so these demand special attention. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. We use the new method of eliminating duplicates in this example. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. The new method of eliminating duplicates in this example has proposed. Using this method, you can get rid of frames that aren't relevant to the video. This makes for more precise and faster video retrieval with fewer duplicates. As a back end, this technique is implemented in C# and SQL. The findings are put to the test and compared to the current SIFT process. The results showed that the output improved accuracy while reducing storage space, computational time, and memory use.


2018 ◽  
Author(s):  
P. Sumathy ◽  
P. Shanmugavadivu ◽  
A. Vadivel

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