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

Bhardwaj, Shikha and Pandove, Gitanjali and Kumar Dahiya, Pawan (2019) An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval. EAI Endorsed Transactions on Scalable Information Systems, 6 (22): e1. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Gray level co-occurence matrix, Discrete wavelet transform, Content-based Image retrieval, Extreme learning machine, Relevance feedback, Brodatz dataset, MIT-Vistex Dataset
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:54
Last Modified: 08 Oct 2020 13:54

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