Image Classification by Web Images

2002 ◽  
pp. 613-614
Author(s):  
Keiji Yanai
Keyword(s):  
Image Classification ◽  
Web Images

Web Image Classification Using an Optimized Feature Set

2005 ◽  
Vol 277-279 ◽  
pp. 361-368
Author(s):  
Soo Sun Cho ◽  
Dong Won Han ◽  
Chi Jung Hwang
Keyword(s):  
Machine Learning ◽  
World Wide Web ◽  
Image Classification ◽  
Decision Trees ◽  
World Wide ◽  
Expressive Power ◽  
Web Pages ◽  
Learning Methods ◽  
Web Images ◽  
The Web

Redundant images currently abundant in World Wide Web pages need to be removed in order to transform or simplify the Web pages for suitable display in small-screened devices. Classifying removable images on the Web pages according to their uniqueness of content will allow simpler representation of Web pages. For such classification, machine learning based methods can be used to categorize images into two groups; eliminable and non-eliminable. We use two representative learning methods, the Naïve Bayesian classifier and C4.5 decision trees. For our Web image classification, we propose new features that have expressive power for Web images to be classified. We apply image samples to the two classifiers and analyze the results. In addition, we propose an algorithm to construct an optimized subset from a whole feature set, which includes most influential features for the purposes of classification. By using the optimized feature set, the accuracy of classification is found to improve markedly.

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Iteration Based Polarimetric SAR Image Classification

PIERS Online ◽  
2007 ◽  
Vol 3 (5) ◽  
pp. 625-628
Author(s):  
Jian Yang ◽  
Xiaoli She ◽  
Tao Xiong
Keyword(s):  
Image Classification ◽  
Sar Image ◽  
Polarimetric Sar
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