scholarly journals Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

Author(s):  
Chen Lin ◽  
Wayne Mak ◽  
Pengyu Hong ◽  
Katharine Sepp ◽  
Norbert Perrimon
2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao

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.


2020 ◽  
Author(s):  
Saliha Mezzoudj

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.


2021 ◽  
Author(s):  
Tijl Grootswagers ◽  
Ivy Zhou ◽  
Amanda K Robinson ◽  
Martin N Hebart ◽  
Thomas A Carlson

The neural basis of object recognition and semantic knowledge have been the focus of a large body of research but given the high dimensionality of object space, it is challenging to develop an overarching theory on how brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. Traditional image databases are based on manually selected object concepts and often single images per concept. In contrast, 'big data' stimulus sets typically consist of images that can vary significantly in quality and may be biased in content. To address this issue, recent work developed THINGS: a large stimulus set of 1,854 object concepts and 26,107 associated images. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to all concepts and 22,248 images in the THINGS stimulus set. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.


Author(s):  
Takanari Tanabata ◽  
◽  
Kazuhito Sawase ◽  
Hajime Nobuhara ◽  
Barnabas Bede ◽  
...  

In order to perform an interactive data-mining for huge image databases efficiently, a visualization interface based on Formal Concept Analysis (FCA) is proposed. The proposed interface system provides an intuitive lattice structure enabling users freely and easily to select FCA attributes and to view different aspects of the Hasse diagram of the lattice of a given image database. The investigation environment is implemented using C++ and the OpenCV library on a personal computer (CPU = 2.13 GHz, MM = 2 GB). In visualization experiments using 1,000 Corel Image Gallery images, we test image features such as color, edge, and face detectors as FCA attributes. Experimental analysis confirms the effectiveness of the proposed interface and its potential as an efficient datamining tool.


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