Practical Application of Near Duplicate Detection for Image Database

2014 ◽  
pp. 73-82 ◽  
Author(s):  
Adi Eshkol ◽  
Michał Grega ◽  
Mikołaj Leszczuk ◽  
Ofer Weintraub
Keyword(s):  
Image Database ◽  
Practical Application ◽  
Duplicate Detection ◽  
Near Duplicate Detection

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.

Pivot-Based Similarity Wide-Joins Fostering Near-Duplicate Detection

2017 ◽  
pp. 81-104
Author(s):  
Luiz Olmes Carvalho ◽  
Lucio Fernandes Dutra Santos ◽  
Agma Juci Machado Traina ◽  
Caetano Traina
Keyword(s):  
Duplicate Detection ◽  
Near Duplicate Detection

Video Query Reformulation for Near-Duplicate Detection

2012 ◽  
Vol 7 (5) ◽  
pp. 1594-1603 ◽  
Author(s):  
Chih-Yi Chiu ◽  
Sheng-Yang Li ◽  
Cheng-Yu Hsieh
Keyword(s):  
Query Reformulation ◽  
Duplicate Detection ◽  
Near Duplicate Detection
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