scholarly journals Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing

2012 ◽  
Vol 47 (15) ◽  
pp. 45-48 ◽  
Author(s):  
H. B.Kekre ◽  
Sudeep Thepade ◽  
Rik Kamal Kumar Das ◽  
Saurav Ghosh
Keyword(s):  
Image Classification ◽  
Block Truncation Coding ◽  
Bit Plane ◽  
Bit Plane Slicing

Edge Detection Using Block Truncation Coding

2003 ◽  
Vol 17 (06) ◽  
pp. 951-966 ◽  
Author(s):  
Yu-Chen Hu ◽  
Chin-Chen Chang
Keyword(s):  
Computational Complexity ◽  
Image Compression ◽  
Edge Detection ◽  
Digital Image ◽  
Block Type ◽  
Detection Scheme ◽  
Block Truncation Coding ◽  
Bit Plane ◽  
Digital Image Compression ◽  
Simple Block

In this paper, a new edge detection scheme based on block truncation coding (BTC) is proposed. As we know, the BTC is a simple and fast scheme for digital image compression. To detect an edge boundary using the BTC scheme, the bit plane information of each BTC-compressed block is exploited, and a simple block type classifier is introduced. The experimental results show that the proposed scheme clearly detects the edge boundaries of digital images while requiring very little computational complexity. Meanwhile, the edge detection process can be incorporated into all BTC variant schemes. In other words, the newly proposed scheme provides a good approach for the detection of edge boundaries using block truncation coding.

scholarly journals VCC-BPS: Vertical Collaborative Clustering using Bit Plane Slicing

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244691
Author(s):  
WAQAR ISHAQ ◽  
ELIYA BUYUKKAYA ◽  
MUSHTAQ ALI ◽  
ZAKIR KHAN
Keyword(s):  
Actual Data ◽  
Common Disease ◽  
Collaborative Clustering ◽  
Data Owner ◽  
Bit Plane ◽  
Class Labels ◽  
A Chain ◽  
Maximum Similarity ◽  
Improved Accuracy ◽  
Bit Plane Slicing

The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students’ performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.

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