Document image database retrieval and browsing using texture analysis

2002 ◽  
Author(s):  
J.F. Cullen ◽  
J.J. Hull ◽  
P.E. Hart
Keyword(s):  
Texture Analysis ◽  
Image Database ◽  
Document Image ◽  
Database Retrieval

Image database retrieval utilizing affinity relationships

2003 ◽  
Author(s):  
Mei-Ling Shyu ◽  
Shu-Ching Chen ◽  
Min Chen ◽  
Chengcui Zhang ◽  
Kanoksri Sarinnapakorn
Keyword(s):  
Image Database ◽  
Database Retrieval

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.

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

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