2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)最新文献_第2页

Efficient and Robust TWSVM Classifier Based on L1-Norm Distance Metric for Pattern Classification 基于l1 -范数距离度量的高效鲁棒TWSVM模式分类器

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.23

He Yan, Qiaolin Ye, Tian'an Zhang, Dong-Jun Yu

引用次数: 0

Robust Lane Detection and Tracking with Propagated Spatio-temporal Constraints 基于传播时空约束的鲁棒车道检测与跟踪

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.92

Tingting Li, Kunqian Li, Wenbing Tao

引用次数: 0

Plagiarism Detection in Programming Assignments Using Deep Features 基于深度特征的编程作业抄袭检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.146

Jitendra Yasaswi, Suresh Purini, C. V. Jawahar

引用次数: 13

Rapid Object Detection in VHR Optical Remote Sensing Images Based on Rotation-Invariant Discrete Hashing 基于旋转不变离散哈希的VHR光学遥感图像快速目标检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.40

Hui Xu, Yazhou Liu, Quansen Sun

{"title":"Rapid Object Detection in VHR Optical Remote Sensing Images Based on Rotation-Invariant Discrete Hashing","authors":"Hui Xu, Yazhou Liu, Quansen Sun","doi":"10.1109/ACPR.2017.40","DOIUrl":"https://doi.org/10.1109/ACPR.2017.40","url":null,"abstract":"Object detection is one of the most fundamental but challenging problems faced for large-scale remote sensing image(RSI) analysis. Recently, learning based hashing techniques have attracted broad research interests because of their significant efficiency for high-dimensional data in both storage and speed. This paper proposes a novel object detection model which utilizes hashing methods to substantially improve the detection speed. In particular, firstly a selective search method is used to generate a number of high-quality object proposals that may contain objects. Then we propose rotation-invariant discrete hashing(RIDISH), which sloves the problem of object rotation variations in RSI, to quickly eliminate most non-object proposals in Hamming space. And finally the object detection task can be achieved by classifying the rest(very limited amount of) proposals with more discriminating classification model. Experimental evaluations on a publicly available very high resolution (VHR) remote sensing dataset point out that the presented object detection model is much faster, while keeping more superior performance than those typically used in VHR remote sensing images.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"32 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":"130067365","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: 0

Adaptive Energy Selection for Content-Aware Image Resizing 自适应能量选择的内容感知图像调整大小

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.105

Kazuma Sasaki, Yuya Nagahama, Zheng Ze, S. Iizuka, E. Simo-Serra, Yoshihiko Mochizuki, H. Ishikawa

{"title":"Adaptive Energy Selection for Content-Aware Image Resizing","authors":"Kazuma Sasaki, Yuya Nagahama, Zheng Ze, S. Iizuka, E. Simo-Serra, Yoshihiko Mochizuki, H. Ishikawa","doi":"10.1109/ACPR.2017.105","DOIUrl":"https://doi.org/10.1109/ACPR.2017.105","url":null,"abstract":"Content-aware image resizing aims to reduce the size of an image without touching important objects and regions. In seam carving, this is done by assessing the importance of each pixel by an energy function and repeatedly removing a string of pixels avoiding pixels with high energy. However, there is no single energy function that is best for all images: the optimal energy function is itself a function of the image. In this paper, we present a method for predicting the quality of the results of resizing an image with different energy functions, so as to select the energy best suited for that particular image. We formulate the selection as a classification problem; i.e., we 'classify' the input into the class of images for which one of the energies works best. The standard approach would be to use a CNN for the classification. However, the existence of a fully connected layer forces us to resize the input to a fixed size, which obliterates useful information, especially lower-level features that more closely relate to the energies used for seam carving. Instead, we extract a feature from internal convolutional layers, which results in a fixed-length vector regardless of the input size, making it amenable to classification with a Support Vector Machine. This formulation of the algorithm selection as a classification problem can be used whenever there are multiple approaches for a specific image processing task. We validate our approach with a user study, where our method outperforms recent seam carving approaches.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"3 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":"131678039","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: 0

Subspace Clustering via Sparse Graph Regularization 基于稀疏图正则化的子空间聚类

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.94

Qiang Zhang, Z. Miao

引用次数: 1

Multi-view Intact Space Clustering 多视图完整空间聚类

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1016/j.patcog.2018.09.016

Ling Huang, Hongyang Chao, Changdong Wang

引用次数: 78

An Efficient Approach for Recognition and Verification of On-Line Signatures Using PSO 基于粒子群算法的在线签名识别与验证方法

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.115

S. Dutta, Rajkumar Saini, Pradeep Kumar, P. Roy

{"title":"An Efficient Approach for Recognition and Verification of On-Line Signatures Using PSO","authors":"S. Dutta, Rajkumar Saini, Pradeep Kumar, P. Roy","doi":"10.1109/ACPR.2017.115","DOIUrl":"https://doi.org/10.1109/ACPR.2017.115","url":null,"abstract":"Signature recognition and verification have been widely used for user authentication. A person is allowed to proceed further only when his/her signature matches with his/her model or template(s) stored in the database. In this paper, a robust approach for online signature recognition and verification has been proposed. Signatures have been segmented into uniform segments and features are collected from each segment to capture local dynamic properties. For signature recognition task, Random Forest is used as the classifier and particle swarm optimization(PSO) has been used to select the best feature-set and model parameter. The feature set and parameters selected from recognition task have been used in training binary random forest classifiers for user verification. Signature verification has been performed in two modes i.e. using global threshold and local threshold and corresponding results have been reported. In our experiment, we have used two public datasets (MCYT-100 and SVC-2004) and have achieved over 99% recognition rate and encouraging Equal Error Rate (EER) for verification on both the datasets.","PeriodicalId":426561,"journal":{"name":"2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"29 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":"130956063","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: 3

Global Abnormal Event Detection Based on Compact Coefficient Low-Rank Dictionary Learning 基于紧凑系数低秩字典学习的全局异常事件检测

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.53

Ang Li, Z. Miao, Yigang Cen

引用次数: 2

Identifying People Using Temporal and Spatial Changes in Local Movements Measured from Body Sway 利用身体摇摆测量的局部运动的时空变化来识别人

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) Pub Date : 2017-11-01 DOI: 10.1109/ACPR.2017.82

T. Kamitani, Hiroki Yoshimura, Masashi Nishiyama, Y. Iwai

引用次数: 1