Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

EMITTER International Journal of Engineering Technology

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Field Value
 
Title Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features
 
Creator Kurniasari, Arvita Agus
Barakbah, Ali Ridho
Basuki, Achmad
 
Subject Image Search; Image Retrieval
Image Feature Extraction; Content-dependent Image Search, Image Search System; Feature Aggregation

 
Description The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels.  For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.
 
Publisher Politeknik Elektronika Negeri Surabaya (PENS)
 
Contributor
 
Date 2019-06-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

 
Format application/pdf
 
Identifier http://emitter.pens.ac.id/index.php/emitter/article/view/361
10.24003/emitter.v7i1.361
 
Source EMITTER International Journal of Engineering Technology; Vol 7, No 1 (2019); 223-242
2443-1168
2355-391X
10.24003/emitter.v7i1
 
Language eng
 
Relation http://emitter.pens.ac.id/index.php/emitter/article/view/361/140
 
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Rights Copyright (c) 2019 EMITTER International Journal of Engineering Technology
http://creativecommons.org/licenses/by-nc-sa/4.0
 

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