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A Fast and Efficient Image Indexing and Search System based on Color and Texture Features

EL Aroussi EL Mehdi, Silkan Hassan, ELhoussif Nourddine
2017 Indian Journal of Science and Technology  
This paper presents a fast and efficient image indexing and search system based on color and texture features.  ...  Color, texture and shape information have been the primitive image descriptors in content-based image retrieval systems.  ...  Therefore, this paper has presented a fast and efficient image indexing and search system based on color and texture features.  ... 
doi:10.17485/ijst/2017/v10i39/119860 fatcat:prxze7pn5za2tgvanubb4qwwra

A Survey on Features and Techniques in Content Based Image Retrieval

Meenu Kalra, Pooja Handa
2015 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
Texture, color, shaped, contours etc are the important entities to represent and search the images. These features of images are extracted and implemented for a similarity check among images.  ...  Among them, content-based image retrieval systems (CBIR) have become very popular for browsing, searching and retrieving images from a large database of digital images as it requires relatively less human  ...  Efficient multidimensional techniques are required for the retrieval system to enable them fast and scaleable.  ... 
doi:10.24297/ijct.v14i10.1829 fatcat:76o4nhzqlremvct7kaj7jtoz7a

Satellite Image retrival based on sensitive content method

Ajit Yadav, R.R sedemkar, harshali patil
2019 International Journal of Recent Trends in Engineering and Research  
In this research, a Content-Based Image Retrieval (CBIR) system has been applied on Geospatial Images of fire and forest, Clutter and water, cyclone and water etc.  ...  The system is robust as it provides search based on the multiple features. The performance of the system was evaluated by analyzing the retrieval results using precision.  ...  Zhi-chun Huang (2010) concentrated on content based image retrieval using color moment and Gabor texture feature which includes combination of color moments of the HSV color space and Gabor texture descriptors  ... 
doi:10.23883/ijrter.2019.5074.89eea fatcat:o2x7ez6rnnb5rhmrfrzuro6jci

Primitive Integration for Content based Image Retrieval

Riaz AhmedShaikh, Jian-Ping Li, Asif Khan
2015 International Journal of Computer Applications  
Texture, color, and shape always a considerable points for extract but need a proper algorithm and model as per image database complexity increase.  ...  Paper work approach based to proposed algorithmic model for efficient and effective retrieval.  ...  Based on texture and color feature of image an efficient CBIR system was presented by Abuhaiba [6] , to explain the image and its coherent parts means segmented regions, said system introduces three options  ... 
doi:10.5120/19822-1659 fatcat:6ocxnwtkyrhgvpi6xutney6c74

Image Retrieval using Classification based on Color

Harsh KumarSarohi, Farhat Ullah Khan
2013 International Journal of Computer Applications  
This paper proposes a new color classification based approach for Image Retrieval.  ...  The proposed method uses two color spaces to form two feature vectors and then uses Euclidean distance to categorize images on the basis of color and which limits the search to specific category.  ...  [4] proposed a scheme based on color-texture and color-histogram. Hiremath and Pujari [5] came up with a CBIR technique based on color, shape and texture. Rao et al.  ... 
doi:10.5120/10678-5557 fatcat:l2qlbouznndfnez5kc7omnmrce

Integration of Color and Texture Features in CBIR System

Hany F., Gamal Attiya, Nawal El-Fishawy
2017 International Journal of Computer Applications  
The proposed methods have shown a promising and faster retrieval on a WANG image database containing 1000 color images.  ...  The current image retrieval system is based on text-based approaches.  ...  [10] presented a Color-Texture and Color-Histogram based Image Retrieval system (CTCHIR).  ... 
doi:10.5120/ijca2017913600 fatcat:mmu4n3at45fkra4n6p6sruguhu

Image Retrieval using Perceptual Hashing

Harsh Kumar Sarohi
2013 IOSR Journal of Computer Engineering  
Content Based Image Retrieval (CBIR) is considered most efficient to search large image collections.  ...  Information in form of images is increasing at a very fast rate therefore there is need for faster and effective retrieval of specific image from image repositories.  ...  Acknowledgement The authors would like to thank Amity School of Engineering and Technology (ASET) where the research was carried out.  ... 
doi:10.9790/0661-0913840 fatcat:3kaht7m6lnfxzfxnriidw7b2pi

A texture extraction technique for cloth pattern identification

Reshmi Abraham, L. K Joshila Grace
2015 Contemporary Engineerng Sciences  
First, our proposed system use color based feature extraction to extract the local and global color features.  ...  In existing system that provides a cloth pattern identification that uses color and texture as visual descriptor to retrieve the similar cloth pattern images.  ...  System implements three image features and a feature selection technique are used in that approach. The first based on color and the second feature is based on texture.  ... 
doi:10.12988/ces.2015.412261 fatcat:yu5cvuwooreq3c7ldiyocpr3eq

Content Based Image Retrieval: Survey and Comparison of CBIR System based on Combined Features

Savita Gandhani, Nandini Singhal
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
In this paper, performance of various CBIR systems, based on combined feature i.e color texture and shape, are compared.  ...  In image processing, computer vision and pattern recognition, the Image retrieval is a most popular research area.  ...  As per our literature survey over texture feature detection, we concluded that BDIP and BVLC were proposed recently and showed a better retrieval efficiency in various domains including content based image  ... 
doi:10.14257/ijsip.2015.8.11.37 fatcat:j7zr22rqdzcfdfbkh5qbaqrdme

Content Based Image Retrieval: Survey and Comparison of CBIR System Based on Combined Features

Savita Gandhani, Nandini Singhal
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
In this paper, performance of various CBIR systems, based on combined feature i.e color texture and shape, are compared.  ...  In image processing, computer vision and pattern recognition, the Image retrieval is a most popular research area.  ...  As per our literature survey over texture feature detection, we concluded that BDIP and BVLC were proposed recently and showed a better retrieval efficiency in various domains including content based image  ... 
doi:10.14257/ijsip.2015.8.10.18 fatcat:w7i2ghfd4bbq7c34vrx5j57wna

Enhancing Capabilities Of Texture Extraction For Color Image Retrieval

Pranam Janney, Sridhar G, V. Sridhar
2007 Zenodo  
In this paper we propose a method for enhancing the capabilities of texture based feature extraction and further demonstrate the use of these enhanced texture features in Texture-Based Color Image Retrieval  ...  Efficient image retrieval with high precision would require an approach which combines usage of both the color and texture features of the image.  ...  The Content-Based Image Retrieval [1] on automatically derived image features. Image features are the visual contents of an image such as color, shape, texture and spatial layout.  ... 
doi:10.5281/zenodo.1078354 fatcat:nm6yoxh2fbbrden3wxxshyhfmu

Content based image retrieval a comparative based analysis for feature extraction approach

Ashwini Vinayak Bhad, Komal Ramteke
2015 2015 International Conference on Advances in Computer Engineering and Applications  
Here we are using an efficient image retrieval technique which uses dynamic dominant color, texture and histogram features of an image.  ...  Image representation based on certain features helps in retrieval process.  ...  Histogram feature can also be extensively used for retrieval systems. So, CBIR system that is based on dominant color, texture and histogram can be implanted .  ... 
doi:10.1109/icacea.2015.7164712 fatcat:n4qllv3vwbg57luzvomz4xvaiq

Feature Selection for Image Retrieval based on Genetic Algorithm

Preeti Kushwaha, Rashmi Welekar
2016 International Journal of Interactive Multimedia and Artificial Intelligence  
This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique.  ...  The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.  ...  The bar chart shows, by using feature selection based on GA takes less time and fast retrieval. It is more efficient and better performance.  ... 
doi:10.9781/ijimai.2016.423 fatcat:p5agoj3vdfa6bbufu7hrcczdym

FAST: Toward more effective and efficient image retrieval

Ruofei Zhang, Zhongfei Mark Zhang
2005 Multimedia Systems  
This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology.  ...  Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into  ...  A region-based retrieval system segments images into regions and retrieves images based on the similarities derived from the regions.  ... 
doi:10.1007/s00530-005-0180-9 fatcat:s2nznybbebawbaial7ootkwdne

Content Based Image Retrieval Using Sketches [chapter]

M. Narayana, Subhash Kulkarni
2013 Advances in Intelligent Systems and Computing  
This paper aims to introduce the problems and challenges concerned with the design and creation of CBIR systems, which is based on a free hand sketch (Sketched based image retrieval-SBIR).  ...  It is also observed that the proposed method achieved low retrieval performance over these four image features for sketch based and color dominant images.  ...  The Sketch-based image retrieval (SBIR) was introduced in QBIC [2] and Visual SEEK [10] systems. In these systems the user draws color sketches and blobs on the drawing area.  ... 
doi:10.1007/978-81-322-0740-5_136 fatcat:5wrm3xrobngvfajhle6h26kqtu
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