Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval最新文献

{"title":"Light Curve Analysis From Kepler Spacecraft Collected Data","authors":"Eduardo Nigri, Ognjen Arandjelovic","doi":"10.1145/3078971.3080544","DOIUrl":"https://doi.org/10.1145/3078971.3080544","url":null,"abstract":"Although scarce, previous work on the application of machine learning and data mining techniques on large corpora of astronomical data has produced promising results. For example, on the task of detecting so-called Kepler objects of interest (KOIs), a range of different `off the shelf' classifiers has demonstrated outstanding performance. These rather preliminary research efforts motivate further exploration of this data domain. In the present work we focus on the analysis of threshold crossing events (TCEs) extracted from photometric data acquired by the Kepler spacecraft. We show that the task of classifying TCEs as being effected by actual planetary transits as opposed to confounding astrophysical phenomena is significantly more challenging than that of KOI detection, with different classifiers exhibiting vastly different performances. Nevertheless, the best performing classifier type, the random forest, achieved excellent accuracy, correctly predicting in approximately 96% of the cases. Our results and analysis should illuminate further efforts into the development of more sophisticated, automatic techniques, and encourage additional work in the area.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765312","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":"A Generic Framework for Social Event Analysis","authors":"Shengsheng Qian, Tianzhu Zhang, Changsheng Xu","doi":"10.1145/3078971.3079007","DOIUrl":"https://doi.org/10.1145/3078971.3079007","url":null,"abstract":"Social event is something that occurs at specific place and time associated with some specific actions, and it consists of many stories over time. With the explosion of Web 2.0 platforms, a popular social event that is happening around us and around the world can spread very fast. As a result, social event analysis becomes more and more important for users to understand the whole evolutionary trend of social event over time. However, it is very challenging to do social event analysis because social event data from different social media sites have multi-modal, multi-domain, and large-scale properties. The goal of our research is to design advanced multimedia techniques to deal with the above issues and establish an effective and robust social event analysis framework for social event representation, detection, tracking and evolution analysis. (1) For social event representation, we propose a novel cross-domain collaborative learning algorithm based on non-parametric Bayesian dictionary learning model. It can make use of the shared domain priors and modality priors to collaboratively learn the data's representations by considering the domain discrepancy and the multi-modal property.(2) For social event detection, we propose a boosted multi-modal supervised Latent Dirichlet Allocation model. It can effectively exploit multi-modality information and utilize boosting weighted sampling strategy for large-scale data processing. (3) For social event tracking, we propose a novel multi-modal event topic model, which can effectively model the correlations between textual and visual modalities, and obtain their topics over time. (4) For social event evolution analysis, we propose a novel multi-modal multi-view topic-opinion mining model to conduct fined-grained topic and opinion analysis for social events from multiple social media sites collaboratively. It can discover multi-modal topics and the corresponding opinions over time to understand the evolutionary processes of social event. Extensive experimental results show that the proposed algorithms perform favorably against state-of-the-art methods for social event analysis.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975622","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":"Utilising High-Level Features in Summarisation of Academic Presentations","authors":"Keith Curtis, G. Jones, N. Campbell","doi":"10.1145/3078971.3079028","DOIUrl":"https://doi.org/10.1145/3078971.3079028","url":null,"abstract":"We present a novel method for the generation of automatic video summaries of academic presentations. We base our investigation on a corpus of multimodal academic conference presentations combining transcripts with paralinguistic multimodal features. We first generate summaries based on keywords by using transcripts created using automatic speech recognition (ASR). Start and end times for each spoken phrase are identified from the ASR transcript, then a value for each phrase created. Spoken phrases are then augmented by incorporating scores for human annotation of paralinguistic features. These features measure audience engagement, comprehension and speaker emphasis. We evaluate the effectiveness of summaries generated for individual presentations, created using speech transcripts and paralinguistic multimodal features, by performing eye-tracking evaluation of participants as they watch summaries and full presentations, and by questionnaire of participants upon completion of eye-tracking studies. Summaries were also evaluated for effectiveness by performing comparisons with an enhanced digital video browser.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131403168","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":"Insiders and Outsiders: Comparing Urban Impressions between Population Groups","authors":"Darshan Santani, S. Ruiz-Correa, D. Gática-Pérez","doi":"10.1145/3078971.3079022","DOIUrl":"https://doi.org/10.1145/3078971.3079022","url":null,"abstract":"There is a growing interest in social and urban computing to employ crowdsourcing as means to gather impressions of urban perception for indoor and outdoor environments. Previous studies have established that reliable estimates of urban perception can be obtained using online crowdsourcing systems, but implicitly assumed that the judgments provided by the crowd are not dependent on the background knowledge of the observer. In this paper, we investigate how the impressions of outdoor urban spaces judged by online crowd annotators, compare with the impressions elicited by the local inhabitants, along six physical and psychological labels. We focus our study in a developing city where understanding and characterization of these socio-urban perceptions is of societal importance. We found statistically significant differences between the two population groups. Locals perceived places to be more dangerous and dirty, when compared with online crowd workers; while online annotators judged places to be more interesting in comparison to locals. Our results highlight the importance of the degree of familiarity with urban spaces and background knowledge while rating urban perceptions, which is lacking in some of the existing work in urban computing.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133656488","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":"A Spatio-Temporal Category Representation for Brand Popularity Prediction","authors":"Gijs Overgoor, M. Mazloom, M. Worring, Robert Rietveld, W. Dolen","doi":"10.1145/3078971.3078998","DOIUrl":"https://doi.org/10.1145/3078971.3078998","url":null,"abstract":"Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated content. Understanding the consumers' attitudes towards a particular brand on social media (i.e. liking) is important. In this paper, we focus on a method for brand popularity prediction and use it to analyze social media posts generated by various brands during a specific period of time. Existing instance-based popularity prediction methods focus on popularity of images, text, and individual posts. We propose a new category based popularity prediction method by incorporating the spatio-temporal dimension in the representation. In particular, we focus on brands as a specific category. We study the behavior of our method by performing four experiments on a collection of brand posts crawled from Instagram with 150,000 posts related to 430 active brands. Our experiments establish that 1) we are able to accurately predict the popularity of posts generated by brands, 2) we can use this post-level trained model to predict the popularity of a brand, 3) by constructing category representations we are improving the accuracy of brand popularity prediction, and 4) using our proposal we are able to select a set of images for each brand with high potential of becoming popular.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124022035","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":"Session details: Oral Session 3: Multimedia Applications (Oral presentations)","authors":"W. Chu","doi":"10.1145/3254622","DOIUrl":"https://doi.org/10.1145/3254622","url":null,"abstract":"","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"187 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124933103","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":"On the Selection of Anchors and Targets for Video Hyperlinking","authors":"Zhi-Qi Cheng, H. Zhang, Xiao Wu, C. Ngo","doi":"10.1145/3078971.3079025","DOIUrl":"https://doi.org/10.1145/3078971.3079025","url":null,"abstract":"A problem not well understood in video hyperlinking is what qualifies a fragment as an anchor or target. Ideally, anchors provide good starting points for navigation, and targets supplement anchors with additional details while not distracting users with irrelevant, false and redundant information. The problem is not trivial for intertwining relationship between data characteristics and user expectation. Imagine that in a large dataset, there are clusters of fragments spreading over the feature space. The nature of each cluster can be described by its size (implying popularity) and structure (implying complexity). A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity. The question is which fragments should be selected either as anchors or targets, in one way to reflect the rich content of a dataset, and meanwhile to minimize the risk of frustrating user experience. This paper provides some insights to this question from the perspective of hubness and local intrinsic dimensionality, which are two statistical properties in assessing the popularity and complexity of data space. Based these properties, two novel algorithms are proposed for low-risk automatic selection of anchors and targets.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122575579","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":"Frame-Transformer Emotion Classification Network","authors":"Jiarui Gao, Yanwei Fu, Yu-Gang Jiang, X. Xue","doi":"10.1145/3078971.3079030","DOIUrl":"https://doi.org/10.1145/3078971.3079030","url":null,"abstract":"Emotional content is a key ingredient in user-generated videos. However, due to the emotion sparsely expressed in the user-generated video, it is very difficult to analayze emotions in videos. In this paper, we propose a new architecture--Frame-Transformer Emotion Classification Network (FT-EC-net) to solve three highly correlated emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization. We also contribute a new dataset for emotion attribution task by annotating the ground-truth labels of attribution segments. A comprehensive set of experiments on two datasets demonstrate the effectiveness of our framework.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130441327","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":"Session details: Demonstrations","authors":"Jaeyoung Choi","doi":"10.1145/3254629","DOIUrl":"https://doi.org/10.1145/3254629","url":null,"abstract":"","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129790449","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":"PACE: Prediction-based Annotation for Crowded Environments","authors":"F. Bartoli, G. Lisanti, Lorenzo Seidenari, A. Bimbo","doi":"10.1145/3078971.3079020","DOIUrl":"https://doi.org/10.1145/3078971.3079020","url":null,"abstract":"We present a new tool we have developed to ease the annotation of crowded environments, typical of visual surveillance datasets. Our tool is developed using HTML5 and Javascript and has two back-ends. A PHP based back-end implement the persistence using a relational database and manage the dynamic creation of pages and the authentication procedure. A python based REST server implement all the computer vision facilities to assist annotators. Our tool allows collaborative annotation of person identity, group membership, location, gaze and occluded parts. PACE supports multiple cameras and if calibration is provided the geometry is used to improve computer vision based assistance. We detail the whole interface comprising an administrative view that ease the setup of the system.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124509190","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}