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2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)最新文献

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Sketch-based image retrieval using contour segments 基于轮廓段的素描图像检索
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340836
Y. Zhang, Xueming Qian, X. Tan
{"title":"Sketch-based image retrieval using contour segments","authors":"Y. Zhang, Xueming Qian, X. Tan","doi":"10.1109/MMSP.2015.7340836","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340836","url":null,"abstract":"The paper presents a sketch-based image retrieval algorithm. One of the main challenges in sketch-based image retrieval (SBIR) is to measure the similarity between a sketch and an image in contour with high precision. To tackle this problem, we divided the contour of image into two types: the first is global contour, suggesting that we can use it to reduce the similarity between the images with complex background. The second, called salient contour, is helpful to retrieve images with objects similar to the query. Besides, we propose a new descriptor, namely angular radial orientation partitioning (AROP) feature, which makes full use of the gradient orientation information to decrease the gap between sketch and image. Using the two contours as candidate contours for feature extraction could increase the retrieval rate dramatically. Finally an application of retrieval system based on this algorithm is established. The experiment on 0.42 million image dataset shows excellent retrieval performance of the proposed method and comparisons with other algorithms are also given.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124685407","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
Speech reconstruction from mel-frequency cepstral coefficients via ℓ1-norm minimization 基于1范数最小化的mel频倒谱系数语音重构
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340799
Gang Min, Xiongwei Zhang, Jibin Yang, Xia Zou
{"title":"Speech reconstruction from mel-frequency cepstral coefficients via ℓ1-norm minimization","authors":"Gang Min, Xiongwei Zhang, Jibin Yang, Xia Zou","doi":"10.1109/MMSP.2015.7340799","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340799","url":null,"abstract":"This paper presents a high quality speech reconstruction method from Mel-frequency cepstral coefficients (MFCC). Due to the sparse characteristic of the power spectrum of speech, the ℓ1-norm minimization method is used to tackle the under-determined nature of the speech reconstruction problem. The phase spectrum is recovered by the well-known LSE-ISTFTM algorithm. Experimental results demonstrate that the quality of the reconstructed speech is dramatically improved than the common ℓ2-norm minimization method, it sounds very close to the original speech when using the high-resolution MFCC, the PESQ score reaches 4.0.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818036","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: 6
Deep kinship verification 深层亲属关系验证
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340820
Mengyin Wang, Zechao Li, Xiangbo Shu, Jingdong Wang, Jinhui Tang
{"title":"Deep kinship verification","authors":"Mengyin Wang, Zechao Li, Xiangbo Shu, Jingdong Wang, Jinhui Tang","doi":"10.1109/MMSP.2015.7340820","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340820","url":null,"abstract":"To improve the performance of kinship verification, we propose a novel deep kinship verification (DKV) model by integrating excellent deep learning architecture into metric learning. Unlike most existing shallow models based on metric learning for kinship verification, we employ a deep learning model followed by a metric learning formulation to select nonlinear features, which can find the appropriate project space to ensure the margin of negative sample pairs (i.e. parent and child without kinship relation) as large as possible and the margin of positive sample pairs (i.e. parent and child with kinship relation) as small as possible. Experimental results show that our method achieves satisfactory performance on two widely-used benchmarks, i.e. KFW-I and KFW-II.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130856925","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: 37
Down-/up-sampling based depth coding by utilizing interview correlation 基于上下采样的深度编码，利用采访相关性
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340870
Jianjun Song, Xin Wang, Yong Wang, Ce Zhu
{"title":"Down-/up-sampling based depth coding by utilizing interview correlation","authors":"Jianjun Song, Xin Wang, Yong Wang, Ce Zhu","doi":"10.1109/MMSP.2015.7340870","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340870","url":null,"abstract":"In depth-based 3D video compression, a framework that down-samples a depth map before encoding and up-samples the depth map at the decoder has shown more high coding efficiency, where the down- and up-sampling components are key to the coding performance. In this paper, a down-/up-sampling scheme for 3D video coding is developed by making good use of interview correlation. At the encoder, to obtain a uniform distribution of sampled pixels across views, an interlaced down-sampling approach is applied among different views. Then, at the decoder, a three-step up-sampling approach is proposed accordingly. First, sampled pixels in one view are warped to the to-be-up-sampled view based on their disparity vectors. Second, in order not to introduce new values, the warped pixel is further refined or updated by one of its nearest four sampled pixels. Finally, the unknown pixels left are filled with a known pixel around it. Among all the three steps, a winner-takes-all approach is employed to select a pixel with the largest weight which is determined by considering the depth similarity, texture similarity or geometric closeness. Experimental results demonstrate that the proposed scheme can achieve better down-/up-sampling result and improved the coding efficiency, compared with the conventional algorithms, especially for a large sampling scale.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858329","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
Audio event recognition based on DBN features from multiple filter-bank representations 基于多滤波器组表示的DBN特征的音频事件识别
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340807
Feng Guo, Xiaoou Chen, Deshun Yang
{"title":"Audio event recognition based on DBN features from multiple filter-bank representations","authors":"Feng Guo, Xiaoou Chen, Deshun Yang","doi":"10.1109/MMSP.2015.7340807","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340807","url":null,"abstract":"In the audio event classification or detection research field, the representation of the audio itself is important. Many researchers tried to apply Deep Belief Network (DBN) to learn new representations of the audio. The mel filter-bank feature, which is obtained based on mel scale, is commonly used as the low level representation of the audio in the pre-processing procedure of DBN. However, the mel bands used in mel filter-bank feature may not be sufficient for the comprehensive representation of the diverse audio events in the real world and then it will make it difficult for DBN to learn good audio features. In this paper, two steps are taken to explore and tackle the problem. In the first step, we conduct a comparison of the effects among different arrangements of frequency bands to DBN feature learning in the audio event recognition. Here the arrangements of frequency bands include mel bands, bark bands, linear bands and pyramid bands. In the second step, in order to utilize the different classification capabilities of the DBN features on different audio events, we adopt the Adaboost algorithm to fuse them. We conduct the experiments on real datasets collected from findsound website, and the results verifies that our proposed audio event classification system, which uses diverse features selected by Adaboost from all sets of DBN features, outperforms the one using only one kind of DBN feature set.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121399452","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
Maximum bit average entropy based binary SIFT (MBAE_BS) for face recognition with pose variation 基于最大比特平均熵的二值SIFT (MBAE_BS)用于姿态变化人脸识别
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340844
Lifang Wu, Wei Qi, Yaxi Hou, Yukun Ma
{"title":"Maximum bit average entropy based binary SIFT (MBAE_BS) for face recognition with pose variation","authors":"Lifang Wu, Wei Qi, Yaxi Hou, Yukun Ma","doi":"10.1109/MMSP.2015.7340844","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340844","url":null,"abstract":"Pose variation is one of challenging problems in face recognition. Complete Pose Binary SIFT(CPBS) [1]has been proposed to extract binary SIFT from face images of five poses. It could resolve the problem of large pose variation. However, in CPBS, each feature is represented as a bit, it possibly brings about information loss. In this paper, we propose maximum bit average entropy algorithm (MBAE) for binarization of CPBS features. And these features are used for face recognition with pose variation. It preserve at most information with the fewer quantization bits. Firstly, the CPBS features are extracted from the gallery images. Secondly, the CPBS features are binarized according to the maximum bit average entropy adaptively. Finally, the distance between a pair of features is computed based on the weighted hamming distance. The compared experimental results on the CMU-PIE and FERET face databases show that our approach is much better than state-of-the-art algorithms.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054104","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
Action recognition using multi-layer Depth Motion maps and Sparse Dictionary Learning 使用多层深度运动地图和稀疏字典学习的动作识别
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340790
Chengwu Liang, E. Chen, L. Qi, L. Guan
{"title":"Action recognition using multi-layer Depth Motion maps and Sparse Dictionary Learning","authors":"Chengwu Liang, E. Chen, L. Qi, L. Guan","doi":"10.1109/MMSP.2015.7340790","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340790","url":null,"abstract":"In this paper, we propose a new spatio-temporal feature based method for human action recognition using depth image sequence. Fist, Layered Depth Motion maps (LDM) are utilized to capture the temporal motion feature. Next, multi-scale HOG descriptors are computed on LDM to characterize the structural information of actions. Then sparse coding is applied for feature representation. Extending Sparse fisher Discriminative Dictionary Learning (SDDL) model and its corresponding classification scheme are also introduced. In SDDL model, the sub-dictionary is updated class by class, leading to class-specific compact discriminative dictionaries. The proposed method is evaluated on public MSR Action3D datasets and demonstrates great performance, especially in cross subject test.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122562450","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: 6
Heaviside image edge sharpening 沉重的图像边缘锐化
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340839
Liang-Jian Deng, Weihong Guo, Tingzhu Huang, Xile Zhao
{"title":"Heaviside image edge sharpening","authors":"Liang-Jian Deng, Weihong Guo, Tingzhu Huang, Xile Zhao","doi":"10.1109/MMSP.2015.7340839","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340839","url":null,"abstract":"In this paper, we propose an automatic and efficient method to enhance edge sharpness of images. Starting from an image with blur edges, we improve the edges using transformed Heaviside functions for better visualization. In addition, we provide an efficient method to directly compute the scaling and shifting factors of the transformed Heaviside functions, so that blur edges can be improved accurately. Experimental results show that the proposed method is fast and can get sharper image edges than some recent state-of-the-art edge enhancement methods. We also apply the edge sharpening method to image super-resolution and obtained promising results.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128479920","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
Perceptual quality assessment of high frame rate video 高帧率视频的感知质量评价
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340831
Rasoul Mohammadi Nasiri, Jiheng Wang, A. Rehman, Shiqi Wang, Zhou Wang
{"title":"Perceptual quality assessment of high frame rate video","authors":"Rasoul Mohammadi Nasiri, Jiheng Wang, A. Rehman, Shiqi Wang, Zhou Wang","doi":"10.1109/MMSP.2015.7340831","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340831","url":null,"abstract":"High frame rate video has been a hot topic in the past few years driven by a strong need in the entertainment and gaming industry. Nevertheless, progress on perceptual quality assessment of high frame rate video remains limited, making it difficult to evaluate the exact perceptual gain by switching from low to high frame rates. In this work, we first conduct a subjective quality assessment experiment on a database that contains videos compressed at different frame rates, quantization levels and spatial resolutions. We then carry out a series of analysis on the subjective data to investigate the impact of frame rate on perceived video quality and its interplay with quantization level, spatial resolution, spatial complexity, and motion complexity. We observe that perceived video quality generally increases with frame rate, but the gain saturates at high rates. Such gain also depends on the interactions between quantization level, spatial resolution, and spatial and motion complexities.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909056","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: 29
Affine SKIP and MERGE modes for video coding 仿射跳过和合并模式的视频编码
2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2015-12-03 DOI: 10.1109/MMSP.2015.7340829
Huanbang Chen, Fan Liang, Sixin Lin
{"title":"Affine SKIP and MERGE modes for video coding","authors":"Huanbang Chen, Fan Liang, Sixin Lin","doi":"10.1109/MMSP.2015.7340829","DOIUrl":"https://doi.org/10.1109/MMSP.2015.7340829","url":null,"abstract":"The latest video coding standard HEVC has already improved 40% coding efficiency than H.264/AVC, but it still follows the hybrid coding structure with the translational motion compensation scheme. In this paper, an affine motion representation is described first. The proposed algorithms to improve the motion vector coding for Affine SKIP/MERGE modes are also presented. The experimental results show that these algorithms can achieve on average 3.5%, 2.2%, and 1.4% BD-rate gains for low delay P, low delay B, and random access configurations, respectively, compared to HM 12.0. The BD-rate saving is up to 17.1% for sequences with complex motion.","PeriodicalId":108577,"journal":{"name":"2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221758","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: 16
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