2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第9页

Fast Genre Classification of Web Images Using Global and Local Features 使用全局和局部特征的Web图像快速类型分类

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.84

Guo-Shuai Liu, Fei Yin, Zhenbo Luo, Cheng-Lin Liu

{"title":"Fast Genre Classification of Web Images Using Global and Local Features","authors":"Guo-Shuai Liu, Fei Yin, Zhenbo Luo, Cheng-Lin Liu","doi":"10.1109/ACPR.2017.84","DOIUrl":"https://doi.org/10.1109/ACPR.2017.84","url":null,"abstract":"A number of images are present on the Web and the number is increasing every day. To effectively mine the contents embedded in Web images, it is useful to classify the images into different types so that they can be fed to different procedures for detailed analysis, such as text and non-text image discrimination. We herein propose a hierarchical algorithm for efficiently classifying Web images into four classes, namely, natural scene images, born-digital images, scanned and cameracaptured paper documents, which are the most prevalent image types on the Web. Our algorithm consists of two stages; the first stage extracts global features reflecting the distributions of color, edge and gradient, and uses a support vector machine (SVM) classifier for preliminary classification. Images assigned low confidence by the first-stage classifier is processed by the second stage, which further extracts local texture features represented in the Bag-of-Words framework and uses another SVM classifier for final classification. In addition, we design two fusion strategies to train the second classifier and generate the final prediction label depending on the usage of local features in the second stage. To validate the effectiveness of our proposed method, we also build a database containing more than 55,000 images from various sources. On our test image set, we obtained an overall classification accuracy of 98.4% and the processing speed is over 27FPS on an Intel(R) Xeon(R) CPU (2.90GHz).","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122338153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Single Image Super-Resolution via Mixed Examples and Sparse Representation 基于混合样例和稀疏表示的单幅图像超分辨率

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.110

Weirong Liu, Changhong Shi, Chaorong Liu, Jie Liu

引用次数: 0

Enlarging Effective Receptive Field of Convolutional Neural Networks for Better Semantic Segmentation 扩大卷积神经网络的有效接受野以实现更好的语义分割

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.7

Yifan Gu, Zuofeng Zhong, Shuai Wu, Yong Xu

引用次数: 4

A Hierarchical Classification Strategy for Robust Detection of Passive/Active Mental State Using User-Voluntary Pitch Imagery Task 基于用户自主音高意象任务的被动/主动心理状态鲁棒检测层次分类策略

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.133

Young-Jin Kee, Min-Ho Lee, J. Williamson, Seong-Whan Lee

{"title":"A Hierarchical Classification Strategy for Robust Detection of Passive/Active Mental State Using User-Voluntary Pitch Imagery Task","authors":"Young-Jin Kee, Min-Ho Lee, J. Williamson, Seong-Whan Lee","doi":"10.1109/ACPR.2017.133","DOIUrl":"https://doi.org/10.1109/ACPR.2017.133","url":null,"abstract":"Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual/auditory stimulation, and it is widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-target stimulus are repeatedly flashed while the electroencephalography (EEG) is recording. ERP responses in the EEG signal have a poor signal-to-ratio in single-trial analysis; therefore, the epochs of the target and non-target trials are averaged over time in order to improve their decoding accuracy. Furthermore, these exogenous potentials can be naturally evoked by just looking at a target symbol. Therefore, the BCI system could generate unintended commands without considering the user's intention. In this study, we approach this dilemma by assuming that a greater effort for the mental task would evoke a stronger positive/negative ERP deflection. Three mental states are defined: passive gazing, active counting, and pitch-imagery. The experiments results showed significantly enhanced ERP patterns and averaged decoding accuracies of 80%, 95.4%, and 95.6%, respectively. The decoding accuracies between both active tasks and the passive task showed an averaged accuracy of 57.5% (gazing vs. counting) and 72.5% (gazing vs. pitch-imagery). Following this result, we proposed a hierarchy classification strategy where the passive or active mental state is decoded in the first stage, and the target stimuli are estimated in the second stage. Our work is the first to propose a system that classifies an intended or unintended brain state by considering the measurable differences of mental effort in the EEG signal so that unintended commands to the system are minimized.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116744420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Complete Dual-Cross Pattern for Unconstrained Texture Classification 一种用于无约束纹理分类的完全双交叉模式

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.160

S. K. Roy, B. Chanda, B. Chaudhuri, D. Ghosh, S. Dubey

{"title":"A Complete Dual-Cross Pattern for Unconstrained Texture Classification","authors":"S. K. Roy, B. Chanda, B. Chaudhuri, D. Ghosh, S. Dubey","doi":"10.1109/ACPR.2017.160","DOIUrl":"https://doi.org/10.1109/ACPR.2017.160","url":null,"abstract":"In order to perform unconstrained texture classification, this paper presents a novel and computationally efficient texture descriptor called Complete Dual-Cross Pattern (CDCP), which is robust to gray-scale changes and surface rotation. To extract CDCP, at first a gray scale normalization scheme is used to reduce the illumination effect and, then CDCP feature is computed from holistic and component levels. A local region of the texture image is represented by it's center pixel and difference of sign-magnitude transform (DSMT) at multiple levels. Using a global threshold, the gray value of center pixel is converted into a binary code named DCP center (DCP_C). DSMT decomposes into two complementary components: the sign and the magnitude. They are encoded respectively into DCP-sign (DCP_S) and DCP-magnitude (DCP_M), based on their corresponding threshold values. Finally, CDCP is formed by fusing DCP_S, DCP_M and DCP_C features through joint distribution. The invariance characteristics of CDCP are attained due to computation of pattern at multiple levels, which makes CDCP highly discriminative and achieves state-of-the-art performance for rotation invariant texture classification.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114297026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Online Optimization and Feedback Elman Neural Network for Maneuvering Target Tracking 机动目标跟踪的在线优化与反馈Elman神经网络

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.56

L. Xia, Ya Zhang, Huajun Liu

引用次数: 1

Probability Based Voting for Vanishing Point Detection 基于概率投票的消失点检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.39

Qian Chen, Kouki Masumoto, Haiyuan Wu, S. Lao

引用次数: 0

Fully Convolutional DenseNet for Saliency-Map Prediction 用于显著性图预测的全卷积密度网络

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.143

Taiki Oyama, Takao Yamanaka

引用次数: 7

Robust Multi-scale ORB Algorithm in Real-Time Monocular Visual Odometry 实时单目视觉里程测量中的鲁棒多尺度ORB算法

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.101

Qiongjie Cui, Huajun Liu

引用次数: 1

Multi-feature Joint Dictionary Learning for Face Recognition 人脸识别的多特征联合字典学习

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.138

Meng Yang, Qiangchang Wang, Wei Wen, Zhihui Lai

引用次数: 3