scholarly journals Block-Based Methods for Image Retrieval Using Local Binary Patterns

2005 ◽  
pp. 882-891 ◽  
Author(s):  
Valtteri Takala ◽  
Timo Ahonen ◽  
Matti Pietikäinen
Keyword(s):  
Image Retrieval ◽  
Local Binary Patterns ◽  
Block Based

scholarly journals A FLEXIBLE SUB-BLOCK IN REGION BASED IMAGE RETRIEVAL BASED ON TRANSITION REGION

2018 ◽  
Vol 11 (1) ◽  
pp. 42
Author(s):  
Ahmad Wahyu Rosyadi ◽  
Renest Danardono ◽  
Siprianus Septian Manek ◽  
Agus Zainal Arifin
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Transition Region ◽  
Local Binary Patterns ◽  
Local Feature ◽  
Global Features ◽  
Query Image ◽  
Block Based ◽  
Hsv Histogram ◽  
Local Feature Extraction

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.

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
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