Object Segmentation Based on the Integration of Adaptive K-means and GrabCut Algorithm

Project Code :TMMAIP412


We propose a method of object segmentation based on hybrid segmentation method means combine use of Adaptive k-means clustering and GrabCut Algorithm


Image segmentation is a well-known topic in image processing, and it remains as a hotspot and focal point for image processing techniques. In this paper, we propose a hybrid segmentation method, combining an Adaptive K-Means clustering algorithm and a novel automatic GrabCut segmentation algorithm to improve the performance of the object segmentation from the scene image. 

The proposed method is divided into six steps: Firstly, the RGB image normalization step is introduced to eliminate light variation and remove bright and shaded regions. Secondly, RGB colour space is converted to L*a*b* colour space to maintain accurate colour balance. Thirdly, we propose a novel automatic GrabCut segmentation algorithm to eliminate user interaction and make the segmentation process faster. Fourthly, the Adaptive K-Means clustering algorithm and the proposed automatic GrabCut segmentation algorithm are combined to segment foreground objects from the background. 

Fifthly, the shape refinement step is used to eliminate occlusion, noise, and smear issues from the segmented image. Finally, morphological operations are carried out to enhance the segmentation performance. The performance of the hybrid segmentation method is assessed using the MSRA benchmark dataset.

Keywords: Segmentation, K-Means clustering, Image processing, Colour space conversion, Image normalization

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


Software: Matlab 2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

·   Introduction to Matlab


·   How to start with MATLAB

·   About Matlab language

·   Matlab coding skills

·   About tools & libraries

·   Application Program Interface in Matlab

·   About Matlab desktop

·   How to use Matlab editor to create M-Files

·   Features of Matlab

·   Basics on Matlab

·   What is an Image/pixel?

·   About image formats

·   Introduction to Image Processing

·   How digital image is formed

·   Importing the image via image acquisition tools

·   Analyzing and manipulation of image.

·   Phases of image processing:

               o  Acquisition

               o  Image enhancement

               o  Image restoration

               o   Color image processing

               o  Image compression

               o   Morphological processing

               o   Segmentation etc.,

·   How to extend our work to another real time applications

·   Project development Skills

               o   Problem analyzing skills

               o   Problem solving skills

               o   Creativity and imaginary skills

               o   Programming skills

               o   Deployment

               o   Testing skills

               o   Debugging skills

               o   Project presentation skills

               o   Thesis writing skills

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