Research Article
Content-Based Image Retrieval Using Local Derivative Laplacian Co-occurrence Pattern
@INPROCEEDINGS{10.1007/978-3-030-82565-2_33, author={Prashant Srivastava and Manish Khare and Ashish Khare}, title={Content-Based Image Retrieval Using Local Derivative Laplacian Co-occurrence Pattern}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={CBIR Image retrieval Laplacian of Gaussian Local Derivative Pattern Gray-Level Co-occurrence pattern}, doi={10.1007/978-3-030-82565-2_33} }- Prashant Srivastava
Manish Khare
Ashish Khare
Year: 2021
Content-Based Image Retrieval Using Local Derivative Laplacian Co-occurrence Pattern
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_33
Abstract
For accessing images from huge repository in an easy manner, the images are required to be properly indexed. Content-Based Image Retrieval (CBIR) is a field which deals with finding solutions to such problems. This paper proposes a new multiresolution descriptor namely, Local Derivative Laplacian Co-occurrence Pattern (LDLCP) for CBIR. Gray level image is subjected to four-level Laplacian of Gaussian filtering in order to perform multiresolution processing of image. Local Derivative Pattern descriptors of resulting four-level filtered image is computed to extract local information from the image. Finally, the Gray-Level Co-occurrence Matrix is used for constructing feature vector. Corel-1K and Corel-5K datasets have been used to test the proposed descriptor and its performance is measured using precision and recall metrics.
