An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Authors

  • Shikha Bhardwaj Kurukshetra University image/svg+xml
  • Gitanjali Pandove Deenbandhu Chhotu Ram University of Science and Technology image/svg+xml
  • Pawan Kumar Dahiya Deenbandhu Chhotu Ram University of Science and Technology image/svg+xml

DOI:

https://doi.org/10.4108/eai.10-6-2019.159344

Keywords:

Gray level co-occurence matrix, Discrete wavelet transform, Content-based Image retrieval, Extreme learning machine, Relevance feedback, Brodatz dataset, MIT-Vistex Dataset

Abstract

Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.

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Published

27-06-2019

How to Cite

1.
Bhardwaj S, Pandove G, Kumar Dahiya P. An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval. EAI Endorsed Scal Inf Syst [Internet]. 2019 Jun. 27 [cited 2024 Nov. 22];6(22):e1. Available from: https://publications.eai.eu/index.php/sis/article/view/2159