Finite prolate spheroidal sequences and their applications. II. Image feature description and segmentation

1988 ◽  
Vol 10 (2) ◽  
pp. 193-203 ◽  
Author(s):  
R. Wilson ◽  
M. Spann
Keyword(s):  
Image Feature ◽  
Prolate Spheroidal ◽  
Feature Description

scholarly journals Segmentation-based Euler number with multi-levels for image feature description

2017 ◽  
Vol 111 ◽  
pp. 245-251
Author(s):  
Qian Zhang ◽  
Lin Wang ◽  
Jiang-Hao Yu ◽  
Minggui Zhang
Keyword(s):  
Euler Number ◽  
Image Feature ◽  
Feature Description

scholarly journals Content-Based Image Feature Description and Retrieving

10.5772/52286 ◽  
2013 ◽  
Author(s):  
Nai-Chung Yang ◽  
Chung-Ming Kuo ◽  
Wei-Han Chang
Keyword(s):  
Image Feature ◽  
Feature Description

scholarly journals Image Clustering using Multichannel Decoded Local Binary Pattern

2019 ◽  
Vol 8 (4) ◽  
pp. 8117-8122
Keyword(s):  
Local Binary Pattern ◽  
Local Binary Patterns ◽  
Image Feature ◽  
Image Clustering ◽  
Image Description ◽  
Feature Description ◽  
Fuzzy C Means Clustering ◽  
Average Recovery Rate ◽  
Novel Method ◽  
Similar Class

CBIR uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. Local Binary Pattern based descriptors have been used for the purpose of image feature description. Local binary pattern (LBP) has widely increased the popularity due to its simplicity and effectiveness in several applications. In this paper, we proposes a novel method for image description with multichannel decoded local binary patterns. Introduce adder and decoder based two schemas for the combination of the LBPs from more than one channel. Finally, uses Fuzzy C-means clustering under semi- supervised framework. The outcomes are processed as far as the normal exactness rate and average recovery rate and improved execution is seen when contrasted and the aftereffects of the current multichannel based methodologies over every database. The component vector is figured for snake and decoder channels utilizing histograms. At long last, the image ordering process is enhanced utilizing information grouping methods for images having a place with a similar class

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