Proceedings of the 24th ACM international conference on Multimedia最新文献_第4页

ConTagNet: Exploiting User Context for Image Tag Recommendation 传染性网络:利用用户上下文进行图像标签推荐

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2984068

Y. Rawat, M. Kankanhalli

引用次数: 67

StressClick: Sensing Stress from Gaze-Click Patterns 压力点击:从凝视点击模式感应压力

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2964318

Michael Xuelin Huang, Jiajia Li, G. Ngai, H. Leong

{"title":"StressClick: Sensing Stress from Gaze-Click Patterns","authors":"Michael Xuelin Huang, Jiajia Li, G. Ngai, H. Leong","doi":"10.1145/2964284.2964318","DOIUrl":"https://doi.org/10.1145/2964284.2964318","url":null,"abstract":"Stress sensing is valuable in many applications, including online learning crowdsourcing and other daily human-computer interactions. Traditional affective computing techniques investigate affect inference based on different individual modalities, such as facial expression, vocal tones, and physiological signals or the aggregation of signals of these independent modalities, without explicitly exploiting their inter-connections. In contrast, this paper focuses on exploring the impact of mental stress on the coordination between two human nervous systems, the somatic and autonomic nervous systems. Specifically, we present the analysis of the subtle but indicative pattern of human gaze behaviors surrounding a mouse-click event, i.e. the gaze-click pattern. Our evaluation shows that mental stress affects the gaze-click pattern, and this influence has largely been ignored in previous work. This paper, therefore, further proposes a non-intrusive approach to inferring human stress level based on the gaze-click pattern, using only data collected from the common computer webcam and mouse. We conducted a human study on solving math questions under different stress levels to explore the validity of stress recognition based on this coordination pattern. Experimental results show the effectiveness of our technique and the generalizability of the proposed features for user-independent modeling. Our results suggest that it may be possible to detect stress non-intrusively in the wild, without the need for specialized equipment.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132013657","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: 36

Context-aware Geometric Object Reconstruction for Mobile Education 情境感知的移动教育几何对象重构

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967244

Jinxin Zheng, Yongtao Wang, Zhi Tang

引用次数: 2

Research Challenges in Developing Multimedia Systems for Managing Emergency Situations 发展多媒体应急管理系统的研究挑战

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2976761

Mengfan Tang, Siripen Pongpaichet, R. Jain

{"title":"Research Challenges in Developing Multimedia Systems for Managing Emergency Situations","authors":"Mengfan Tang, Siripen Pongpaichet, R. Jain","doi":"10.1145/2964284.2976761","DOIUrl":"https://doi.org/10.1145/2964284.2976761","url":null,"abstract":"With an increasing amount of diverse heterogeneous data and information, the methodology of multimedia analysis has become increasingly relevant in solving challenging societal problems such as managing emergency situations during disasters. Using cybernetic principles combined with multimedia technology, researchers can develop effective frameworks for using diverse multimedia (including traditional multimedia as well as diverse multimodal) data for situation recognition, and determining and communicating appropriate actions to people stranded during disasters. We present known issues in disaster management and then focus on emergency situations. We show that an emergency management problem is fundamentally a multimedia information assimilation problem for situation recognition and for connecting people's needs to available resources effectively, efficiently, and promptly. Major research challenges for managing emergency situations are identified and discussed. We also present a intelligently detecting evolving environmental situations, and discuss the role of multimedia micro-reports as spontaneous participatory sensing data streams in emergency responses. Given enormous progress in concept recognition using machine learning in the last few years, situation recognition may be the next major challenge for learning approaches in multimedia contextual big data. The data needed for developing such approaches is now easily available on the Web and many challenging research problems in this area are ripe for exploration in order to positively impact our society during its most difficult times.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121429516","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: 18

Synthesizing Emerging Images from Photographs 从照片合成新兴图像

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967304

Cheng-Han Yang, Ying-Miao Kuo, Hung-Kuo Chu

{"title":"Synthesizing Emerging Images from Photographs","authors":"Cheng-Han Yang, Ying-Miao Kuo, Hung-Kuo Chu","doi":"10.1145/2964284.2967304","DOIUrl":"https://doi.org/10.1145/2964284.2967304","url":null,"abstract":"Emergence is the visual phenomenon by which humans recognize the objects in a seemingly noisy image through aggregating information from meaningless pieces and perceiving a whole that is meaningful. Such an unique mental skill renders emergence an effective scheme to tell humans and machines apart. Images that are detectable by human but difficult for an automatic algorithm to recognize are also referred as emerging images. A recent state-of-the-art work proposes to synthesize images of 3D objects that are detectable by human but difficult for an automatic algorithm to recognize. Their results are further verified to be easy for humans to recognize while posing a hard time for automatic machines. However, using 3D objects as inputs prevents their system from being practical and scalable for generating an infinite number of high quality images. For instance, the image quality may degrade quickly as the viewing and lighting conditions changing in 3D domain, and the available resources of 3D models are usually limited. However, using 3D objects as inputs brings drawbacks. For instance, the quality of results is sensitive to the viewing and lighting conditions in the 3D domain. The available resources of 3D models are usually limited, and thus restricts the scalability. This paper presents a novel synthesis technique to automatically generate emerging images from regular photographs, which are commonly taken with decent setting and widely accessible online. We adapt the previous system to the 2D setting of input photographs and develop a set of image-based operations. Our algorithm is also designed to support the difficulty level control of resultant images through a limited set of parameters. We conducted several experiments to validate the efficacy and efficiency of our system.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091633","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

Processing-Aware Privacy-Preserving Photo Sharing over Online Social Networks 在线社交网络上的处理感知隐私保护照片共享

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967288

Weiwei Sun, Jiantao Zhou, Ran Lyu, Shuyuan Zhu

引用次数: 21

Demand-adaptive Clothing Image Retrieval Using Hybrid Topic Model 基于混合主题模型的需求自适应服装图像检索

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967270

Zhengzhong Zhou, Jingjin Zhou, Liqing Zhang

引用次数: 9

Binary Optimized Hashing 二进制优化哈希

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2964331

Qi Dai, Jianguo Li, Jingdong Wang, Yu-Gang Jiang

{"title":"Binary Optimized Hashing","authors":"Qi Dai, Jianguo Li, Jingdong Wang, Yu-Gang Jiang","doi":"10.1145/2964284.2964331","DOIUrl":"https://doi.org/10.1145/2964284.2964331","url":null,"abstract":"This paper studies the problem of learning to hash, which is essentially a mixed integer optimization problem, containing both the binary hash code output and the (continuous) parameters forming the hash functions. Different from existing relaxation methods in hashing, which have no theoretical guarantees for the error bound of the relaxations, we propose binary optimized hashing (BOH), in which we prove that if the loss function is Lipschitz continuous, the binary optimization problem can be relaxed to a bound-constrained continuous optimization problem. Then we introduce a surrogate objective function, which only depends on unbinarized hash functions and does not need the slack variables transforming unbinarized hash functions to discrete functions, to approximate the relaxed objective function. We show that the approximation error is bounded and the bound is small when the problem is optimized. We apply the proposed approach to learn hash codes from either handcraft feature inputs or raw image inputs. Extensive experiments are carried out on three benchmarks, demonstrating that our approach outperforms state-of-the-arts with a significant margin on search accuracies.","PeriodicalId":140670,"journal":{"name":"Proceedings of the 24th ACM international conference on Multimedia","volume":"44 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983798","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: 34

Supervised Recurrent Hashing for Large Scale Video Retrieval 大规模视频检索的监督循环哈希

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967225

Yun Gu, Chao Ma, Jie Yang

引用次数: 42

Exploiting Hierarchical Activations of Neural Network for Image Retrieval 利用层次激活神经网络进行图像检索

Proceedings of the 24th ACM international conference on Multimedia Pub Date : 2016-10-01 DOI: 10.1145/2964284.2967197

Ying Li, Xiangwei Kong, Liang Zheng, Q. Tian

引用次数: 29