2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)最新文献_第10页

Salient object segmentation using a switch scheme 突出目标分割使用切换方案

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820712

Ran Shi, K. Ngan, Songnan Li

引用次数: 0

Style-oriented landmark retrieval and summarization 面向风格的地标检索与总结

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820857

Wei-Yi Chang, Yi-Ren Yeh, Y. Wang

引用次数: 3

Dynamic convolutional neural network for activity recognition 动态卷积神经网络用于活动识别

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820749

Chih-Hsiang You, Chen-Kuo Chiang

引用次数: 2

Image copy-move forgery detection using hierarchical feature point matching 基于分层特征点匹配的图像复制-移动伪造检测

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820758

Yuanman Li, Jiantao Zhou

引用次数: 12

Computer-assisted pronunciation training: From pronunciation scoring towards spoken language learning 计算机辅助发音训练:从发音评分到口语学习

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820782

Nancy F. Chen, Haizhou Li

引用次数: 35

A novel paragraph embedding method for spoken document summarization 一种新的语音文档摘要段落嵌入方法

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820882

Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen, H. Wang

{"title":"A novel paragraph embedding method for spoken document summarization","authors":"Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen, H. Wang","doi":"10.1109/APSIPA.2016.7820882","DOIUrl":"https://doi.org/10.1109/APSIPA.2016.7820882","url":null,"abstract":"Representation learning has emerged as a newly active research subject in many machine learning applications because of its excellent performance. In the context of natural language processing, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as information retrieval and document summarization. However, as far as we are aware, there is only a dearth of research focusing on launching paragraph embedding methods. Extractive spoken document summarization, which can help us browse and digest multimedia data efficiently, aims at selecting a set of indicative sentences from a source document to express the most important theme of the document. A general consensus is that relevance and redundancy are both critical issues in a realistic summarization scenario. However, most of the existing methods focus on determining only the relevance degree between a pair of sentence and document. Motivated by these observations, three major contributions are proposed in this paper. First, we propose a novel unsupervised paragraph embedding method, named the essence vector model, which aims at not only distilling the most representative information from a paragraph but also getting rid of the general background information to produce a more informative low-dimensional vector representation. Second, we incorporate the deduced essence vectors with a density peaks clustering summarization method, which can take both relevance and redundancy information into account simultaneously, to enhance the spoken document summarization performance. Third, the effectiveness of our proposed methods over several well-practiced and state-of-the-art methods is confirmed by extensive spoken document summarization experiments.","PeriodicalId":409448,"journal":{"name":"2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122445","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

Travel photo album summarization based on aesthetic quality, interestingness, and memorableness 旅游相册总结基于审美质量，趣味性，和记忆性

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820889

Jun-Hyuk Kim, Jong-Seok Lee

引用次数: 3

A discriminative training method incorporating pronunciation variations for dysarthric automatic speech recognition 一种结合发音变化的辨别性训练方法用于困难语音自动识别

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820840

Woo Kyeong Seong, Nam Kyun Kim, H. Ha, H. Kim

引用次数: 2

Sparse spatial filtering in frequency domain of multi-channel EEG for frequency and phase detection 多通道脑电图频域稀疏空间滤波用于频率和相位检测

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820779

Naoki Morikawa, Toshihisa Tanaka

引用次数: 2

A fast multi-focus image fusion algorithm by DWT and focused region decision map 一种基于小波变换和聚焦区域决策图的快速多焦点图像融合算法

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) Pub Date : 2016-12-01 DOI: 10.1109/APSIPA.2016.7820864

Shumin Liu, Jiajia Chen

{"title":"A fast multi-focus image fusion algorithm by DWT and focused region decision map","authors":"Shumin Liu, Jiajia Chen","doi":"10.1109/APSIPA.2016.7820864","DOIUrl":"https://doi.org/10.1109/APSIPA.2016.7820864","url":null,"abstract":"To comprise the advantages of both the spatial domain and transform domain methods, this paper presents a novel hybrid algorithm for multi-focus images fusion, which reduces the error rate of sub-band coefficients selection in the transform domain and reduce the artificial discontinuities created in the spatial domain algorithms. In this method, wavelet transforms are firstly performed on each input image, and a focused region decision map is established based on the high-frequency sub-bands extraction. The fusion rules are then guided by this map, and the fused coefficients are transformed back to form the fused image. Experimental results demonstrate that the proposed method is better than various existing methods, in term of fusion quality benchmarks. In addition, the proposed algorithm has a complexity proportional to the total number of pixels in the image, which is lower than some other algorithm which may produce similar fusion quality with the proposed algorithm. Furthermore, the proposed algorithm only requires one level wavelet decomposition, again reducing the processing time. With the proposed method, high quality and fast multi-focus image fusion is made possible.","PeriodicalId":409448,"journal":{"name":"2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123233314","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: 13