A Novel Machine-Learning
Framework-based on LBP and GLCM Approaches for CBIR System
Meenakshi Garg1, Manisha Malhotra1,
and Harpal Singh2
1University Institute of Computing, Chandigarh University, India
2Department of Electronics and Communication Engineering, CEC
Landran, India
Abstract: This paper
presents a Multiple-features extraction and reduction-based approaches for
Content-Based Image Retrieval (CBIR). Discrete Wavelet Transforms (DWT) on
colored channels is used to decompose the image at multiple stages. The Gray
Level Co-occurrence Matrix (GLCM) concept is used to extract statistical
characteristics for texture image classification. The definition of shared
knowledge is used to classify the most common features for all COREL dataset
groups. These are also fed into a feature selector based on the particle swarm
optimization which reduces the number of features that can be used during the
classification stage. Three classifiers, called the Support Vector Machine (SVM),
K-Nearest Neighbor (KNN) and Decision Tree (DT), are trained and tested, in
which SVM give high classification accuracy and precise rates. In several of
the COREL dataset types, experimental findings have demonstrated above 94
percent precision and 0.80 to 0.90 precision values.
Keywords: CBIR, DWT, SHO, feature
selection, classification.
Received November 18, 2019; accepted July
20, 2020