Generic orthogonal moments: Jacobi–Fourier moments for invariant image description

2007 ◽  
Vol 40 (4) ◽  
pp. 1245-1254 ◽  
Author(s):  
Ziliang Ping ◽  
Haiping Ren ◽  
Jian Zou ◽  
Yunlong Sheng ◽  
Wurigen Bo
Keyword(s):  
Image Description ◽  
Orthogonal Moments

scholarly journals Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1544
Author(s):  
Chunpeng Wang ◽  
Hongling Gao ◽  
Meihong Yang ◽  
Jian Li ◽  
Bin Ma ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Fractional Order ◽  
Continuous Functions ◽  
Kernel Functions ◽  
Superior Performance ◽  
Image Description ◽  
Orthogonal Moments ◽  
Integer Order ◽  
Geometric Invariance ◽  
Order Continuous

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper. Firstly, the radial polynomials of integer-order PHFMs are modified to obtain fractional-order radial polynomials, and FrPHFMs are constructed based on the fractional-order radial polynomials; subsequently, the strong reconstruction ability, orthogonality, and geometric invariance of the proposed FrPHFMs are proven; and, finally, the performance of the proposed FrPHFMs is compared with that of integer-order PHFMs, fractional-order radial harmonic Fourier moments (FrRHFMs), fractional-order polar harmonic transforms (FrPHTs), and fractional-order Zernike moments (FrZMs). The experimental results show that the FrPHFMs constructed in this paper are superior to integer-order PHFMs and other fractional-order continuous orthogonal moments in terms of performance in image reconstruction and object recognition, as well as that the proposed FrPHFMs have strong image description ability and good stability.

scholarly journals Accurate Computation of Fractional-Order Exponential Moments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Shujiang Xu ◽  
Qixian Hao ◽  
Bin Ma ◽  
Chunpeng Wang ◽  
Jian Li
Keyword(s):  
Numerical Integration ◽  
Fractional Order ◽  
Numerical Instability ◽  
Calculation Methods ◽  
Image Description ◽  
Accurate Calculation ◽  
Orthogonal Moments ◽  
Instability Problem ◽  
Exponential Moments ◽  
Numerical Integration Error

Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.

A Fast Non-destructive Algorithm for Image Description Based on Improved Wavelet Moment Features

2009 ◽  
Vol 35 (10) ◽  
pp. 1278-1282
Author(s):  
Jia-Min LIU ◽  
Hai-Jun XIE ◽  
Qiang LIU ◽  
Sheng-Jun ZHU ◽  
Wei ZHANG
Keyword(s):  
Image Description ◽  
Moment Features ◽  
Non Destructive

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

Deep sequential fusion LSTM network for image description

2018 ◽  
Vol 312 ◽  
pp. 154-164 ◽  
Author(s):  
Pengjie Tang ◽  
Hanli Wang ◽  
Sam Kwong
Keyword(s):  
Image Description ◽  
Lstm Network

Suggesting text alternatives for images in social media

2021 ◽  
pp. 1-6
Author(s):  
Letícia Seixas Pereira ◽  
João Guerreiro ◽  
André Rodrigues ◽  
André Santos ◽  
João Vicente ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Shared Responsibility ◽  
User Generated Content ◽  
Media Content ◽  
Android Application ◽  
Image Description ◽  
Visual Content ◽  
Considerable Part ◽  
The Web

Image description has been a recurrent topic on web accessibility over the years. With the increased use of social networks, this discussion is even more relevant. Social networks are responsible for a considerable part of the images available on the web. In this context, users are not only consuming visual content but also creating it. Due to this shared responsibility of providing accessible content, major platforms must go beyond accessible interfaces. Additional resources must also be available to support users in creating accessible content. Although many of today's services already support accessible media content authoring, current efforts still fail to properly integrate and guide their users through the authoring process. One of the consequences is that many users are still unaware of what an image description is, how to provide it, and why it is necessary. We present SONAAR, a project that aims to improve the accessibility of user-generated content on social networks. Our approach is to support the authoring and consumption of accessible social media content. Our prototypes currently focus on Twitter and Facebook and are available as an Android application and as a Chrome extension.

Sign in / Sign up

Export Citation Format

Share Document