scholarly journals Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy

2017 ◽  
Vol 115 ◽  
pp. 440-447 ◽  
Author(s):  
Gajanan M. Galshetwar ◽  
Laxman M. Waghmare ◽  
Anil B. Gonde ◽  
Subrahmanyam Murala
Keyword(s):  
Diabetic Retinopathy ◽  
Image Retrieval ◽  
Local Binary Patterns

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

2016 ◽  
Vol 25 (9) ◽  
pp. 4018-4032 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Satish Kumar Singh ◽  
Rajat Kumar Singh
Keyword(s):  
Image Retrieval ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval

Statistical classifiers on local binary patterns for optical diagnosis of diabetic retinopathy

2018 ◽  
Author(s):  
Prasanta K. Panigrahi ◽  
Sabyasachi Mukhopadhyay ◽  
Sawon Pratiher ◽  
Jay Chhablani ◽  
Sukanya Mukherjee ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Local Binary Patterns ◽  
Optical Diagnosis ◽  
Statistical Classifiers
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