Shen-Chi Chen, Chia-Hsiang Wu, Shih-Yao Lin, Y. Hung
{"title":"2D Face Alignment and Pose Estimation Based on 3D Facial Models","authors":"Shen-Chi Chen, Chia-Hsiang Wu, Shih-Yao Lin, Y. Hung","doi":"10.1109/ICME.2012.60","DOIUrl":"https://doi.org/10.1109/ICME.2012.60","url":null,"abstract":"Face alignment and head pose estimation has become a thriving research field with various applications for the past decade. Several approaches process on 2D texture image but most of them perform decently only with small pose variation. Recently, many approaches apply depth information to align objects. However, applications are restricted because depth cameras are more expensive than common cameras, and many original image resources contain no depth information. Therefore, we propose a 3D face alignment algorithm in 2D image based on Active Shape Model, and use Speeded-Up Robust Features (SURF) descriptors as local texture model. We train a 3D shape model with different view-based local texture models from a 3D database, and then fit a face in a 2D image by these models. We also improve the performance by two-stage search strategy. Furthermore, the head pose can be estimated by the alignment result of the proposed 3D model. Finally, we demonstrate some applications applied by our method.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129215","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}
{"title":"Optimal Bit-allocation for Wavelet-based Scalable Video Coding","authors":"Guan-Ju Peng, W. Hwang, Sao-Jie Chen","doi":"10.1109/ICME.2012.146","DOIUrl":"https://doi.org/10.1109/ICME.2012.146","url":null,"abstract":"We investigate the wavelet-based scalable video coding problem and present a solution that takes account of each user's preferred resolution. Based on the preference, we formulate the bit allocation problem of wavelet-based scalable video coding. We propose three methods to solve the problem. The first is an efficient Lagrangian-based method that solves the upper bound of the problem optimally, and the second is a less efficient dynamic programming method that solves the problem optimally. Both methods require knowledge of the user's preference. For the case where the user's preference is unknown, we solve the problem by a min-max approach. Our objective is to find the bit allocation solution that maximizes the worst possible performance. We show that the worst performance occurs when all users subscribe to the same spatial, temporal, and quality resolutions. Thus, the min-max solution is exactly the same as the traditional bit allocation method for a non-scalable wavelet codec. We conduct several experiments on the 2D+t MCTF-EZBC wavelet codec with respect to various subscribers' preferences. The results demonstrate that knowing the users' preferences improves the coding performance of the scalable video codec significantly.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"224 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134289394","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}
{"title":"Ontological Inference Framework with Joint Ontology Construction and Learning for Image Understanding","authors":"S. Tsai, Hao Tang, Feng Tang, Thomas S. Huang","doi":"10.1109/ICME.2012.145","DOIUrl":"https://doi.org/10.1109/ICME.2012.145","url":null,"abstract":"Lack of human prior knowledge is one of the main reasons that semantic gap still remains when it comes to automatic multimedia understanding. In this work, we exploit the ontological structure of target concepts and propose an universal ontological inference framework for image understanding. The framework explicitly utilizes subclass and co-occurrence relation to effectively refine the coarse concept detections. Moreover, we show how to automatically construct and learn the underlying ontology required by the framework. As can be shown by experiments, the result is an effective and robust algorithm that characterizes well the structure of the target concepts and outperforms the state-of-the-art methods.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036593","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}
Johann Poignant, L. Besacier, G. Quénot, F. Thollard
{"title":"From Text Detection in Videos to Person Identification","authors":"Johann Poignant, L. Besacier, G. Quénot, F. Thollard","doi":"10.1109/ICME.2012.119","DOIUrl":"https://doi.org/10.1109/ICME.2012.119","url":null,"abstract":"We present in this article a video OCR system that detects and recognizes overlaid texts in video as well as its application to person identification in video documents. We proceed in several steps. First, text detection and temporal tracking are performed. After adaptation of images to a standard OCR system, a final post-processing combines multiple transcriptions of the same text box. The semi-supervised adaptation of this system to a particular video type (video broadcast from a French TV) is proposed and evaluated. The system is efficient as it runs 3 times faster than real time (including the OCR step) on a desktop Linux box. Both text detection and recognition are evaluated individually and through a person recognition task where it is shown that the combination of OCR and audio (speaker) information can greatly improve the performances of a state of the art audio based person identification system.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"1988 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125491116","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}
{"title":"Comparison of Curvelet and Wavelet Texture Features for Content Based Image Retrieval","authors":"I. Sumana, Guojun Lu, Dengsheng Zhang","doi":"10.1109/ICME.2012.90","DOIUrl":"https://doi.org/10.1109/ICME.2012.90","url":null,"abstract":"Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134371735","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}
{"title":"An Adaptive Dynamic Scheduling Scheme for H.264/AVC Decoding on Multicore Architecture","authors":"Dung Vu, Jilong Kuang, L. Bhuyan","doi":"10.1109/ICME.2012.9","DOIUrl":"https://doi.org/10.1109/ICME.2012.9","url":null,"abstract":"Parallelizing H.264/AVC decoding on multicore architectures is challenged by its inherent structural and functional dependencies at both frame and macro-block levels, as macro-blocks and certain frame types must be decoded in a sequential order. So far, dynamic scheduling scheme with recursive tail submit, as one of the best existing algorithms, provides a good throughput performance by exploiting macro-block level parallelism and mitigating global queue contention. Nevertheless, it fails to achieve an optimal performance due to 1) the use of global queue, which incurs substantial synchronization overhead when the number of cores increases and 2) the unawareness of cache locality with respect to the underlying hierarchical core/cache topology that results in unnecessary latency, communication cost and load imbalance. In this paper, we propose an adaptive dynamic scheduling scheme that employs multiple local queues to reduce lock contention, and assigns tasks in a cache locality aware and load-balancing fashion so that neighboring macro-blocks are preferably dispatched to nearby cores. We design, implement and evaluate our scheme on a 32-core cc-NUMA SGI server. Compared to existing alternatives by running real benchmark applications, we observe that our scheme produces higher throughput and lower latency with more balanced workload and less communication cost.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134286065","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}
{"title":"Grouplet-Based Distance Metric Learning for Video Concept Detection","authors":"Wei Jiang, A. Loui","doi":"10.1109/ICME.2012.123","DOIUrl":"https://doi.org/10.1109/ICME.2012.123","url":null,"abstract":"We investigate general concept detection in unconstrained videos. A distance metric learning algorithm is developed to use the information of the group let structure for improved detection. A group let is defined as a set of audio and/or visual code words that are grouped together according to their strong correlations in videos. By using the entire group lets as building elements, concepts can be more robustly detected than using discrete audio or visual code words. Compared with the traditional method of generating aggregated group let-based features for classification, our group let-based distance metric learning approach directly learns distances between data points, which better preserves the group let structure. Specifically, our algorithm uses an iterative quadratic programming formulation where the optimal distance metric can be effectively learned based on the large-margin nearest-neighbor setting. The framework is quite flexible, where various types of distances can be computed using individual group lets, and through the same distance metric learning algorithm the distances computed over individual group lets can be combined for final classification. We extensively evaluate our method over the large-scale Columbia Consumer Video set. Experiments demonstrate that our approach can achieve consistent and significant performance improvements.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132390661","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}
Yanjie Li, Tianfan Xue, Lifeng Sun, Jianzhuang Liu
{"title":"Joint Example-Based Depth Map Super-Resolution","authors":"Yanjie Li, Tianfan Xue, Lifeng Sun, Jianzhuang Liu","doi":"10.1109/ICME.2012.30","DOIUrl":"https://doi.org/10.1109/ICME.2012.30","url":null,"abstract":"The fast development of time-of-flight (ToF) cameras in recent years enables capture of high frame-rate 3D depth maps of moving objects. However, the resolution of depth map captured by ToF is rather limited, and thus it cannot be directly used to build a high quality 3D model. In order to handle this problem, we propose a novel joint example-based depth map super-resolution method, which converts a low resolution depth map to a high resolution depth map, using a registered high resolution color image as a reference. Different from previous depth map SR methods without training stage, we learn a mapping function from a set of training samples and enhance the resolution of the depth map via sparse coding algorithm. We further use a reconstruction constraint to make object edges sharper. Experimental results show that our method outperforms state-of-the-art methods for depth map super-resolution.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133020389","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}
{"title":"LIPS: A Lightweight Inter-layer Protection Scheme for Scalable Video Coding","authors":"Shih-Ying Chang, Hsin-Ta Chiao","doi":"10.1109/ICME.2012.33","DOIUrl":"https://doi.org/10.1109/ICME.2012.33","url":null,"abstract":"Scalable Video Coding (SVC) enables the partition of video data into layers of different priorities for adapting to both the diversities of terminal capabilities and the variation of network transmission mediums. It is more desirable that more important video data can be recovered with higher probabilities. In this paper, we propose a lightweight inter-layer protection scheme for SVC transmission, which enables the data in a higher layer (e.g., an enhancement layer) to enhance the data reconstruction in a lower layer (e.g., a base layer) in the receiver side. In contrast to non-inter-layer protection schemes, the simulation result in a DVB-H broadcast channel shows that the scheme exhibits 1% to 45% enhancement on the reconstruction of the SVC video data in the base layer.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928693","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}
{"title":"Lumipen: Projection-Based Mixed Reality for Dynamic Objects","authors":"Kohei Okumura, H. Oku, M. Ishikawa","doi":"10.1109/ICME.2012.34","DOIUrl":"https://doi.org/10.1109/ICME.2012.34","url":null,"abstract":"Recently, mixed reality (MR) involving the use of a projector, which is often called projection-based MR, has attracted much attention and is being widely studied. However, most MR research is mainly focused on static objects or situations. In contrast, we propose \"Lumipen,\" which is a projection-based MR system for high-speed or high-frequency objects, using a high-speed vision sensor and a projector with a high-speed optical gaze controller. The high-speed vision sensor detects the dynamics of the object, and the high-speed optical gaze controller controls the projection direction in a few milliseconds. Lumipen is expected to enable addition of sophisticated and diverse visual information even in dynamic live content (e.g., a play, a sports game, a music concert, etc.), like computer graphics added to video content.","PeriodicalId":273567,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116826809","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}