Algebraic Geometry and Computational Algebraic Geometry for Image Database Indexing, Image Recognition, And Computer Vision

1999 ◽  
Author(s):  
Peter Stiller
Keyword(s):  
Computer Vision ◽  
Algebraic Geometry ◽  
Image Recognition ◽  
Image Database ◽  
Computational Algebraic Geometry ◽  
Database Indexing

2D Image Database Indexing: A Coefficient-Based Approach

2007 ◽  
Author(s):  
Xiaoqian Jiang ◽  
Wanhong Xu ◽  
Yiheng Li ◽  
Latanya Sweeney ◽  
Ralph Gross ◽  
...  
Keyword(s):  
Image Database ◽  
Database Indexing

Image database indexing using JPEG coefficients

2002 ◽  
Vol 35 (11) ◽  
pp. 2479-2488 ◽  
Author(s):  
Sharlee Climer ◽  
Sanjiv K. Bhatia
Keyword(s):  
Image Database ◽  
Database Indexing

Unsupervised and Interactive Semi-supervised Clustering for Large Image Database Indexing and Retrieval

2014 ◽  
Vol 130 (2) ◽  
pp. 201-218
Author(s):  
Hien Phuong Lai ◽  
Muriel Visani ◽  
Alain Boucher ◽  
Jean-Marc Ogier
Keyword(s):  
Image Database ◽  
Supervised Clustering ◽  
Indexing And Retrieval ◽  
Database Indexing

scholarly journals Human-Like Sketch Object Recognition via Analogical Learning

2019 ◽  
Vol 33 ◽  
pp. 1336-1343
Author(s):  
Kezhen Chen ◽  
Irina Rabkina ◽  
Matthew D. McLure ◽  
Kenneth D. Forbus
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Image Recognition ◽  
Visual Representations ◽  
Learning Systems ◽  
Training Data ◽  
Adversarial Examples ◽  
Analogical Learning

Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.

A new interactive semi-supervised clustering model for large image database indexing

2014 ◽  
Vol 37 ◽  
pp. 94-106 ◽  
Author(s):  
Hien Phuong Lai ◽  
Muriel Visani ◽  
Alain Boucher ◽  
Jean-Marc Ogier
Keyword(s):  
Image Database ◽  
Supervised Clustering ◽  
Clustering Model ◽  
Database Indexing
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