Earticle

초록

영어

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Method
 4. Feature Extraction
  4.1 Colour
  4.2 Texture
  4.3 Shape
 5. Similarity Measure
  5.1 Minimum Distance Algorithm
 6. Experimental Results
 7. Conclusion and Future Work
 References

키워드

저자

• E. R. Vimina [ Department of Computer Science, Rajagiri College of Social Sciences, Kerala, India ]
• K. Poulose Jacob [ Department of Computer Science, CUSAT, Kerala, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605