Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval

2004 ◽  
Author(s):  
Xuejie Qin ◽  
Yee-Hong Yang
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Gray Level ◽  
Texture Image

An Ensemble of Feature Extraction with Whale Optimization Algorithm for Content Based Image Retrieval System

2020 ◽  
Vol 17 (12) ◽  
pp. 5386-5398
Author(s):  
P. Sasikumar ◽  
K. Venkatachalapathy
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Similarity Measure ◽  
Optimization Algorithm ◽  
Similarity Index ◽  
Whale Optimization Algorithm ◽  
Content Based Image Retrieval ◽  
Gray Level ◽  
Query Image ◽  
Whale Optimization

In recent days, content based image retrieval (CBIR) becomes a hot research area, which aims to determine the relevant images to the query image (QI) from the available large sized database. This paper presents an optimal hybrid feature extraction with similarity measure (OHFE-SM) for CBIR. Initially, histogram equalization of images takes place as a preprocessing step. Then, texture, shape and color features are extracted. The texture features include Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is extracted, where the optimal number of features will be chosen by whale optimization algorithm (WOA). Afterwards, the shape feature extraction takes place by Crest lines and color feature extraction process will be carried out using Quaternion moments. Finally, Euclidean distance will be applied as a similarity measure to determine the distance among the feature vectors exist in the database and QI. The images with higher similarity index will be considered as relevant images and is retrieved from the database. A detailed experimental validation takes place against Corel10K dataset. The simulation results showed that the proposed OHFE-SM model has outperformed the existing methods with the higher average precision of 0.915 and recall of 0.780.

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

2014 ◽  
Vol 40 (8) ◽  
pp. 154-162 ◽  
Author(s):  
Shailendrakumar M. Mukane ◽  
Sachin R. Gengaje ◽  
Dattatraya S. Bormane
Keyword(s):  
Fuzzy Logic ◽  
Image Retrieval ◽  
Texture Image ◽  
Rotation Invariant ◽  
Retrieval Method

scholarly journals WRITER IDENTIFICATION BY TEXTURE ANALYSIS BASED ON KANNADA HANDWRITING

2012 ◽  
pp. 80-85
Author(s):  
B.V. DHANDRA ◽  
VIJAYALAXMI.M. B ◽  
GURURAJ MUKARAMBI ◽  
MALLIKARJUN. HANGARGE
Keyword(s):  
Texture Analysis ◽  
Identification Problem ◽  
Document Image ◽  
Independent Method ◽  
Gray Level ◽  
Writer Identification ◽  
Texture Image ◽  
Major Research ◽  
Gabor Filtering ◽  
Occurrence Matrix

Writer identification problem is one of the important area of research due to its various applications and is a challenging task. The major research on writer identification is based on handwritten English documents with text independent and dependent. However, there is no significant work on identification of writers based on Kannada document. Hence, in this paper, we propose a text-independent method for off-line writer identification based on Kannada handwritten scripts. By observing each individual’s handwriting as a different texture image, a set of features based on Discrete Cosine Transform, Gabor filtering and gray level co-occurrence matrix, are extracted from preprocessed document image blocks. Experimental results demonstrate that the Gabor energy features are more potential than the DCTs and GLCMs based features for writer identification from 20 people.

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