scholarly journals A Hybrid Local and Server-Based Large Scale Image Retrieval System and its Applications

2018 ◽  
Vol 72 (1) ◽  
pp. 106-112
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Image Retrieval System ◽  
Large Scale Image Retrieval

scholarly journals Towards Large Scale Image Retrieval System Using Parallel Frameworks

2020 ◽  
Author(s):  
Saliha Mezzoudj
Keyword(s):  
Big Data ◽  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Content Search ◽  
Parallel Frameworks ◽  
Image Retrieval System ◽  
Large Scale Image Retrieval

Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

scholarly journals A Hybrid Local and Server-Based Large Scale Image Retrieval System and its Applications

2015 ◽  
Vol 69 (1) ◽  
pp. J11-J16
Author(s):  
Arei Kobayashi ◽  
Masaaki Matsumoto ◽  
Yusuke Uchida ◽  
Wataru Doi ◽  
Kouhei Matsuzaki ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Image Retrieval System ◽  
Large Scale Image Retrieval

A Novel Image Retrieval System Based on Histogram Factorization and Contextual Similarity Learning

2013 ◽  
Vol 380-384 ◽  
pp. 4148-4151 ◽  
Author(s):  
Sivakolundu Jayasekara ◽  
Hithanadura Dassanayake ◽  
Anil Fernando
Keyword(s):  
Image Retrieval ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Retrieval System ◽  
Nonnegative Matrix ◽  
Bag Of Words ◽  
Similarity Learning ◽  
Query Image ◽  
Image Retrieval System

Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.

scholarly journals An Efficient Clustering Based CBIR System using Hadoop to Analyze Massive MRI Images Dataset for Early Disease Diagnosis

2019 ◽  
Vol 9 (1) ◽  
pp. 270-278
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Large Scale ◽  
Retrieval System ◽  
Medical Images ◽  
Disease Diagnosis ◽  
Early Disease ◽  
Image Retrieval System ◽  
Hadoop Platform ◽  
Magnetic Resonance Imaging Mri

In past decade, the use of medical imaging for disease diagnosis is increased rapidly. The medical images provide useful information about the anatomy of patients. The medical images are used not only to assist the doctors for diagnosis purpose, but also used in Research & Development for deeper insights and better understanding into cause and cure of numerous diseases. To retrieve medical images from large scale repositories in real time, there is urgent need of an efficient medical image retrieval system. For this purpose, an efficient clustering based content based image retrieval (CBIR) system using Hadoop is proposed to analyze massive magnetic resonance imaging (MRI) images dataset for early disease diagnosis. The proposed CBIR system uses Hadoop platform, local mesh peak valley edge patterns (LMePVEP) for feature extraction, MapReduce based parallel k-means algorithm for clustering and euclidean distance to measure similarity. The method proposed is tested and compared with state-of-the-art CBIR methods on massive MRI images dataset. The experimental results obtained show that the method proposed in our work outperforms traditional CBIR methods in terms of average retrieval time and mean average precision for massive MRI images dataset

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