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논문검색

Clustering Amelioration and Optimization with Swarm Intelligence for Color Image Segmentation

초록

영어

Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms. The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1. K-Means Clustering
  2.2. Artificial Bee Colony
  2.3. ANT Colony Optimization
 3. Proposed Approach
  3.1. Modified K-Means
  3.2. ABCMK-Means
  3.3. ACOMK-Means
 4. Implementation and Results
  4.1. Accuracy
  4.2. Sensitivity
  4.3. Specificity
  4.4. F-Measure
  4.5. Bit Error Rate
  4.6. Execution Time
 5. Conclusion and Future Work
 References

저자정보

  • Kiranpreet M.tech CSE Dept., CT group of engg., mgmt.& tech., Asst.proff. M.tech CSE Dept.,CT group of engg.,mgmt.&tech. Jalandhar(India)
  • Prince Verma M.tech CSE Dept., CT group of engg., mgmt.& tech., Asst.proff. M.tech CSE Dept.,CT group of engg.,mgmt.&tech. Jalandhar(India)

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