Select Subject

All (8830)

Agronomy (158)

Animal Husbandry,... (64)

Aquatic Science (16)

Astronomy (36)

Aviation (22)

Basic Medicine (184)

Biology (504)

Chemical Engineering (103)

Chemistry (635)

Civil and Archite... (105)

Clinical Medicine (236)

Computer Science ... (924)

Dynamic and Elect... (121)

Earth Science (119)

Economics (67)

Education (31)

Electrics, Commun... (927)

Energy Science an... (67)

Engineering and T... (15)

Environmental Sci... (140)

Food Science and ... (63)

Forestry (36)

Hydraulic Enginee... (40)

Mechanics (240)

Metallurgical Eng... (32)

Mine Engineering ... (38)

Nuclear Science a... (21)

Pharmacy (98)

Physics (920)

Preventive Medici... (37)

Psychology (20)

Security Science ... (17)

Sports Science (6)

Surveying and Map... (15)

Textile Science a... (16)

Traditional Chine... (56)

Transportation En... (57)

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list


	1. Improved Face Super-Resolution GenerativeAdversarial Networks
	WANG Mengxue, Zhenxue Chen,Zhenxue Chen,ZHOU Xinjie
	Computer Science and Technology 28 June 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:The face super-resolution method is used for generating high-resolution images from low-resolution ones for better visualization. The Super-Resolution Generative Adversarial Network (SRGAN) can generate a single super-resolution image with realistic textures, which is a groundbreaking work. Based on SRGAN, we propose improved face super-resolution generative adversarial networks. The super-resolution image details generated by SRGAN usually have undesirable artifacts. To further improve visual quality, we delve into the key components of the SRGAN network architecture and improve each part to achieve a more powerful SRGAN. First, the SRGAN employs residual blocks as the core of the very deep generator network G. In this paper, we decide to employ Dense Convolutional Network blocks (Dense blocks), which connect each layer to every other layer in a feed-forward fashion as our very deep generator networks. Moreover, in the past few years, generative adversarial networks (GANs) have been applied to solve various problems. Despite its superior performance, however, it is difficult to train. A simple and effective regularization method called spectral normalization GAN (SNGAN) is used to solve this problem. We have experimentally confirmed that our proposed method is superior to the other existing method in training stability and visual improvements.
	TO cite this article:WANG Mengxue, Zhenxue Chen,Zhenxue Chen, et al. Improved Face Super-Resolution GenerativeAdversarial Networks[OL].[28 June 2019] http://en.paper.edu.cn/en_releasepaper/content/4749261


	2. Feature Selection Method Guided by Attention Mechanism for Image Classification
	Zang Hao,Huang Yaping,Tian Mei,Tian Mei,Tian Mei,1,1,1,1,1,1,1,1
	Computer Science and Technology 24 July 2018
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In recent years, significant progresses have been made in the field of deep learning, especially in visual image classification. The features learned automatically by feed-forward deep convolutional neural networks (CNNs) are important for image classification. However, there is not much research on the spatial relationship of images in despite of a huge amount of work on constructing different network structures to improve classification accuracy. Therefore, in this paper, we propose a method for classifying images with saliency information and background information. We demonstrate that both saliency features and background features have an important influence on image classification. We firstly obtain attention heat map and features from CNN network. Secondly, we separate the features into saliency features and background features inspired by attention heat map. Then, we adopt several pooling strategies to process saliency and background features. Finally, we classify image by training a SVM classifier. Especially, we get effective improvements in Calthech-256 with 78.15\% accuracy and PASCAL VOC 2012 with 84.1\% mAP, demonstrating the effectiveness of our proposed method.
	TO cite this article:Zang Hao,Huang Yaping,Tian Mei, et al. Feature Selection Method Guided by Attention Mechanism for Image Classification[OL].[24 July 2018] http://en.paper.edu.cn/en_releasepaper/content/4745721


	3. Vehicle-Logo Recognition Method based on Local Binary Pattern
	Liang Dong,Zhang Honggang,Shi Kuan,Hou Chengwen
	Computer Science and Technology 08 October 2014
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Vehicle-logo location and recognition has a high research value for intelligent transportation system. With the emergence of car deck, only license plate recognition has become unreliable for automated management traffic. The reliability of the vehicle identification will greatly improve when combining license plate information with vehicle information. In order to solve vehicle-logo location and recognition, the paper presents a new method of vehicle-logo location based on license plate location. Then the Local Binary Pattern(LBP) descriptor computed over an vehicle-logo image as feature vector, then similarity between test vehicle-logo image and template images calculated by Euclidean distance.And the experiments have a good recognition rate.
	TO cite this article:Liang Dong,Zhang Honggang,Shi Kuan, et al. Vehicle-Logo Recognition Method based on Local Binary Pattern[OL].[ 8 October 2014] http://en.paper.edu.cn/en_releasepaper/content/4612650


	4. An image similarity measure based on corner
	Li Dehua,Zhu Jingchao,Yang Zhi
	Computer Science and Technology 07 January 2013
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:This paper presents a method to compare an image with a set of images and rank the images by their similarity. The common way either uses time-cost features or a set of training examples to get arguments, this method is fast and similar with text comparison. Firstly, we get all images' corner points by chord-to-point distance accumulation (CPDA), then generate their feature vectors by corners which can overcome different lightness, rotation and scale, finally compute the similarity between the images by their feature vectors, while the similarity function provides two arguments to fit into different applications. The experimental results tell us that the precision and performance both perform very well. As this method is kind of like text comparison, so it is well-defined for the image search engine.
	TO cite this article:Li Dehua,Zhu Jingchao,Yang Zhi. An image similarity measure based on corner[OL].[ 7 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4511424


	5. Frameworks for Multimodal Biometric using Sparse Representation
	Huang Zengxi,Liu Yiguang,Huang Ronggang,Yang Menglong
	Computer Science and Technology 04 January 2013
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:This paper will introduce three frameworks of two fusion levels for multimodal biometric using sparse representation based classification (SRC), which has been successfully used in many classification tasks recently. The first framework is multimodal SRC at match score level (MSRC_s), in which feature of each modality is sparsely coded independently, and then their representation fidelities are used as match scores for multimodal classification. The other two frameworks are of multimodal SRC at feature level, namely MSRC_f1 and MSRC_f2, where features of all modalities are first fused and then classified by using SRC. The difference between them is that MSRC_f1 fuses the features to form a unique multimodal feature vector, while MSRC_f2 implicitly combines the features in an iterative joint sparse coding process. As a typical application, the fusion of face and ear for human identification is investigated by using the three frameworks. Many results demonstrate that the proposed multimodal methods are significantly better than the multimodal recognition using common classifiers. Among the SRC based methods, MSRC_s gets the top recognition accuracy in almost all the test items, which might benefit from allowing sparse coding independence for different modalities.
	TO cite this article:Huang Zengxi,Liu Yiguang,Huang Ronggang, et al. Frameworks for Multimodal Biometric using Sparse Representation[OL].[ 4 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4511813


	6. Automatic age estimation via sparse representation
	LIANG Yixiong,LIU Lingbo
	Computer Science and Technology 09 February 2012
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Automatic age estimation from face has received increasing attention due to its wide range of application. A successful age estimator typically consists of two key modules: age-related feature extraction and age estimation by regression or classification. In this paper we propose a novel age estimator method based on sparse representation. In the feature exaction stage, the mid-level Spatial-Pyramid face representation based on Sparse codes of SIFT features (ScSPM) is used to characterize the age-related variance. For age estimation, linear sparse regression models are learned which can not only select the most discriminative features but also perform the age estimation. The hierarchical strategy, which first coarsely classifies the faces into age groups and then finely estimates the detailed age by the linear regression model of this group, is adopted to deal with the non-linearity attribute of aging to improve the performance of the age regression model. To our best knowledge, it is the first time to apply ScSPM and sparse linear regression to age estimation. The experimental results show that the proposed approach outperforms the state-of-the-art on the FG-NET database and achieves competitive performance on the MORPH database.
	TO cite this article:LIANG Yixiong,LIU Lingbo. Automatic age estimation via sparse representation[OL].[ 9 February 2012] http://en.paper.edu.cn/en_releasepaper/content/4464426


	7. A Multi-feature Descriptor for E-Commodity Image Retrieval
	Yu Jie,Zhang Honggang,Li Qiaohong,Chai Lunshao,Qi Yonggang
	Computer Science and Technology 13 October 2011
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:With the development of the 3G network and Electronic Commerce, more and more people prefer to shop online, especially via the Cell phone equipped with camera. In order to meet the requirements of the consumers to easily retrieve similar commodity images, we propose a Mobile Image-to-Search system. In this system a content-based retrieval algorithm combining color and shape features for electronic commerce has been investigated, which uses the Normalized Fourier Descriptor and a fuzzy-linking method of color histogram based on the HSV color space. In order to reduce the interference of background, we also use the method of improved canny descriptor before extracting the shape features and the approach of ROI (region of interest) weighted in the color histogram. The system has been tested on Corel data set and our own commodity library, a large number of images selected from Taobao, one of the largest Electronic Commerce web in China. The experimental results indicate that our method has a better retrieval performance.
	TO cite this article:Yu Jie,Zhang Honggang,Li Qiaohong, et al. A Multi-feature Descriptor for E-Commodity Image Retrieval[OL].[13 October 2011] http://en.paper.edu.cn/en_releasepaper/content/4444419

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list

Rank

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item