Optimizing Topic Distributions of Descriptions for Image Description Translation

2018 ◽  
pp. 293-304
Author(s):  
Jian Tang ◽  
Yu Hong ◽  
Mengyi Liu ◽  
Jiashuo Zhang ◽  
Jianmin Yao
Keyword(s):  
Image Description

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

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.

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.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description

2018 ◽  
Vol 27 (1) ◽  
pp. 394-405 ◽  
Author(s):  
Ke Gu ◽  
Vinit Jakhetiya ◽  
Jun-Fei Qiao ◽  
Xiaoli Li ◽  
Weisi Lin ◽  
...  
Keyword(s):  
Image Description ◽  
Quality Metric ◽  
Model Based ◽  
Local Image

scholarly journals Energy analysis of image descriptors in texture classification

2021 ◽  
Author(s):  
Faeze Kiani
Keyword(s):  
Surface Defect ◽  
Energy Analysis ◽  
Texture Classification ◽  
Medical Image Analysis ◽  
Image Description ◽  
Image Descriptors ◽  
Discriminative Feature ◽  
Surface Defect Detection ◽  
Occurrence Matrix

Texture play important role in image description process. Texture classification is one of the problems which have been paid much attention on by computer vision scientists in last decade. If texture classification is done accurately, it can be used in many problems such as skin detection, surface defect detection, medical image analysis, gender identification, human identification, etc. Since now, many approaches are proposed to perform it. Most of them have tried to extract discriminative features to separate different texture types accurately. This paper has proposed an approach based on energy analysis of some efficient image descriptors such as median binary pattern, Local binary pattern and Gray Level Co-occurrence matrix. Next, by concatenating extracted features, a discriminative feature vector is defined. Finally, classifier is used to classify texture types. Although, this approach is a general one and is could be used in different applications. In the result part the proposed approach has been evaluated on some benchmark dataset. Next, the results have been compared with some of state-of-the-art approaches to prove the quality of the proposed approach.

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