An Efficient Content-Based Image Retrieval (CBIR) Using GLCM for Feature Extraction

  • P. ChandanaEmail author
  • P. Srinivas Rao
  • C. H. Satyanarayana
  • Y. Srinivas
  • A. Gauthami Latha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 555)


Today, modern technology led to a faster growth of digital media collection, and it contains both still images and videos. Storage devices contain large amount of digital images, increasing the response time of a system to retrieve images required from such collections, which degrades the performance. Various search skills are needed to find what we are searching for in such large collections. The annotations are given manually for images by describing with the set of keywords. By doing so, the contents of an image retrieve images of interest, but it is time-consuming. Also, different individuals may annotate the same image using different keywords, which make it difficult to create a suitable classification and annotate images with the exact keywords. To overcome all these reasons, content-based image retrieval (CBIR) is the area which is used for extracting images. The technique gray-level co-occurrence matrix (GLCM) is discussed and analyzed for retrieval of image. It considers the various features such as color histogram, texture, and edge density. In this paper, we mainly concentrate on texture feature for accurate and effective content-based image retrieval system.




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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • P. Chandana
    • 1
    Email author
  • P. Srinivas Rao
    • 2
  • C. H. Satyanarayana
    • 3
  • Y. Srinivas
    • 4
  • A. Gauthami Latha
    • 1
  1. 1.Department of Computer Science EngineeringMESBhogapuramIndia
  2. 2.Department of Computer Science EngineeringAUVisakhapatnamIndia
  3. 3.Department of CSEJNTUKakinadaIndia
  4. 4.Department of ITGITAM UniversityVisakhapatnamIndia

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