Logarithmic distance measure with improved local vector pattern for content-based image retrieval

2018 ◽  
Vol 66 (4) ◽  
pp. 239-253
Author(s):  
Jatothu Brahmaiah Naik ◽  
Giri Babu Kande ◽  
Chanamallu Srinivasarao
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Content Based Image Retrieval ◽  
Local Vector ◽  
Logarithmic Distance ◽  
Local Vector Pattern

Local vector pattern with global index angles for a content‐based image retrieval system

2017 ◽  
Vol 68 (12) ◽  
pp. 2755-2770 ◽  
Author(s):  
Jatothu Brahmaiah Naik ◽  
Chanamallu Srinivasarao ◽  
Giri Babu Kande
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
Global Index ◽  
Image Retrieval System ◽  
Local Vector ◽  
Local Vector Pattern

Image Retrieval of Self-Adapt Distance Measure Based on SLLE

2014 ◽  
Vol 989-994 ◽  
pp. 3675-3678
Author(s):  
Xiao Fen Wang ◽  
Hai Na Zhang ◽  
Xiu Rong Qiu ◽  
Jiang Ping Song ◽  
Ke Xin Zhang
Keyword(s):  
Image Retrieval ◽  
High Dimension ◽  
Topological Structure ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Content Based Image Retrieval ◽  
Locally Linear Embedding ◽  
Euclidean Distance Measure ◽  
Linear Embedding ◽  
Dimension Space

Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.

scholarly journals Performance Analysis of Distance Metric for Content Based Image Retrieval

2019 ◽  
Vol 8 (6) ◽  
pp. 2215-2218
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Image Features ◽  
Distance Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Vectors ◽  
City Block Distance ◽  
The City

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

scholarly journals Content Based Geographic Image Retrieval using Local Vector Pattern

2018 ◽  
Vol 61 (0) ◽  
Author(s):  
A. Jenitta ◽  
R. Samson Ravindran
Keyword(s):  
Image Retrieval ◽  
Local Vector ◽  
Local Vector Pattern

A New Content-Based Image Retrieval Using the Multidimensional Generalization of Wald-Wolfowitz Runs Test

2009 ◽  
Vol 129 (1) ◽  
pp. 94-102 ◽  
Author(s):  
Thurdsak Leauhatong ◽  
Kazuhiko Hamamoto ◽  
Kiyoaki Atsuta ◽  
Shozo Kondo
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Multidimensional Generalization

Fault map query using content-based image retrieval

2014 ◽  
Author(s):  
Jie Ma ◽  
Xiaomei Zhang ◽  
Zhiwei Zhang
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval

Color Image Retrieval Based on Chernoff Distance Measure

2018 ◽  
Vol 6 (9) ◽  
pp. 329-333
Author(s):  
S.Selvaraj . ◽  
K. Seetharaman
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Color Image ◽  
Chernoff Distance

FIXED CONTENT BASED IMAGE RETRIEVAL TECHNIQUE

2017 ◽  
Vol 5 (3) ◽  
pp. 54
Author(s):  
MOHAMMED ILIAS SHAIK ◽  
CHAUHAN DINESH ◽  
ESAPALLI SRINIVAS ◽  
PADIGE VINEETH ◽  
◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Technique
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