Large-Scale Face Image Retrieval System at Attribute Level Based on Facial Attribute Ontology and Deep Neuron Network

2018 ◽  
pp. 539-549 ◽  
Author(s):  
Hung M. Nguyen ◽  
Ngoc Q. Ly ◽  
Trang T. T. Phung
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Retrieval System ◽  
Face Image ◽  
Attribute Level ◽  
Neuron Network ◽  
Image Retrieval System

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.

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 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

An Express Retrieval Method of Face Based on Semantic Features

2013 ◽  
Vol 850-851 ◽  
pp. 905-908
Author(s):  
Ming Zhang ◽  
Hai Wei Mu ◽  
Xiang Lou Liu ◽  
Dong Yan Zhao
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Recognition Rate ◽  
Face Image ◽  
Image Process ◽  
Semantic Features ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Image Retrieval System ◽  
Face Pattern

The paper uses digital image processing technology, technology of face pattern recognition and traditional database retrieval technology, integrate image retrieval technology based on version and content, and avoid the complexity of matching image process. The experiments with 200 human samples, correctly retrieved for 155 people, exactly matched to 125. The recognition rate of the system is 75.55%, the average time of search is less than 0.1s. Experiments indicate this method has strong robustness. The semantic face image retrieval system using this method has the characteristics of fast, efficient, practical.

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