Research on Commodity Image Classification Based on Local Oppugnant Color Vector Angle Pattern

2020 ◽  
Author(s):  
Huadong Sun ◽  
Mingda Zhang ◽  
Xu Zhang ◽  
Xiaowei Han ◽  
Yang Liu
Keyword(s):  
Image Classification ◽  
Color Vector ◽  
Vector Angle

Image Digest using Color Vector Angle and Dominant Walsh-Hadamard Transform Coefficients

2021 ◽  
Author(s):  
Naveena Budda ◽  
K. Meenakshi ◽  
Padmavathi Kora ◽  
G.V. Subba Reddy ◽  
K. Swaraja
Keyword(s):  
Hadamard Transform ◽  
Color Vector ◽  
Transform Coefficients ◽  
Walsh Hadamard Transform ◽  
Vector Angle

Image hashing with color vector angle

2018 ◽  
Vol 308 ◽  
pp. 147-158 ◽  
Author(s):  
Zhenjun Tang ◽  
Xuelong Li ◽  
Xianquan Zhang ◽  
Shichao Zhang ◽  
Yumin Dai
Keyword(s):  
Image Hashing ◽  
Color Vector ◽  
Vector Angle

scholarly journals Color treatment in endoscopic image classification using multi-scale local color vector patterns

2012 ◽  
Vol 16 (1) ◽  
pp. 75-86 ◽  
Author(s):  
M. Häfner ◽  
M. Liedlgruber ◽  
A. Uhl ◽  
A. Vécsei ◽  
F. Wrba
Keyword(s):  
Image Classification ◽  
Local Color ◽  
Multi Scale ◽  
Endoscopic Image ◽  
Color Vector ◽  
Color Treatment

Content-based image retrieval using color vector angle difference histogram

2017 ◽  
Vol 40 (3) ◽  
pp. 246-256
Author(s):  
Huadong Sun ◽  
Zhijie Zhao ◽  
Qin Tian ◽  
Xuesong Jin ◽  
Lizhi Zhang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Angle Difference ◽  
Color Vector ◽  
Vector Angle ◽  
Difference Histogram

Robust image hashing based on color vector angle and Canny operator

2016 ◽  
Vol 70 (6) ◽  
pp. 833-841 ◽  
Author(s):  
Zhenjun Tang ◽  
Liyan Huang ◽  
Xianquan Zhang ◽  
Huan Lao
Keyword(s):  
Image Hashing ◽  
Canny Operator ◽  
Color Vector ◽  
Robust Image ◽  
Vector Angle ◽  
Robust Image Hashing

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.

Sign in / Sign up

Export Citation Format

Share Document