Image Database Navigation on a Hierarchical MDS Grid

2006 ◽  
pp. 304-313 ◽  
Author(s):  
Gerald Schaefer ◽  
Simon Ruszala
Keyword(s):  
Image Database

The Image Database Management System of Teaching File Using Personal Computer

1995 ◽  
Vol 32 (4) ◽  
pp. 677
Author(s):  
M J Shin ◽  
G W Kim ◽  
T J Chun ◽  
W H Ahn ◽  
S K Balk ◽  
...  
Keyword(s):  
Personal Computer ◽  
Management System ◽  
Database Management ◽  
Image Database ◽  
Database Management System ◽  
Teaching File

scholarly journals CASIA-Face-Africa: A Large-scale African Face Image Database

2021 ◽  
pp. 1-1
Author(s):  
Jawad Muhammad ◽  
Yunlong Wang ◽  
Caiyong Wanga ◽  
Kunbo Zhang ◽  
Zhenan Sun
Keyword(s):  
Large Scale ◽  
Image Database ◽  
Face Image

Image database retrieval utilizing affinity relationships

2003 ◽  
Author(s):  
Mei-Ling Shyu ◽  
Shu-Ching Chen ◽  
Min Chen ◽  
Chengcui Zhang ◽  
Kanoksri Sarinnapakorn
Keyword(s):  
Image Database ◽  
Database Retrieval

scholarly journals Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Goodwin ◽  
Sanket Padmanabhan ◽  
Sanchit Hira ◽  
Margaret Glancey ◽  
Monet Slinowsky ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Novel Species ◽  
Novelty Detection ◽  
Mosquito Species ◽  
Image Database ◽  
Closed Set ◽  
Open Set

AbstractWith over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

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