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Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing最新文献

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Shape Representations for Maya Codical Glyphs: Knowledge-driven or Deep? 玛雅楔形符号的形状表示:知识驱动还是深度驱动?
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095746
G. Can, J. Odobez, D. Gática-Pérez
{"title":"Shape Representations for Maya Codical Glyphs: Knowledge-driven or Deep?","authors":"G. Can, J. Odobez, D. Gática-Pérez","doi":"10.1145/3095713.3095746","DOIUrl":"https://doi.org/10.1145/3095713.3095746","url":null,"abstract":"This paper investigates two-types of shape representations for individual Maya codical glyphs: traditional bag-of-words built on knowledge-driven local shape descriptors (HOOSC), and Convolutional Neural Networks (CNN) based representations, learned from data. For CNN representations, first, we evaluate the activations of typical CNNs that are pretrained on large-scale image datasets; second, we train a CNN from scratch with all the available individual segments. One of the main challenges while training CNNs is the limited amount of available data (and handling data imbalance issue). Here, we attempt to solve this imbalance issue by introducing class-weights into the loss computation during training. Another possibility is oversampling the minority class samples during batch selection. We show that deep representations outperform the other, but CNN training requires special care for small-scale unbalanced data, that is usually the case in the cultural heritage domain.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122125822","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
JORD: A System for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery JORD:透过连结社会媒体与卫星影像来收集资讯与监测自然灾害的系统
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095726
Kashif Ahmad, M. Riegler, Konstantin Pogorelov, N. Conci, P. Halvorsen, F. D. Natale
{"title":"JORD: A System for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery","authors":"Kashif Ahmad, M. Riegler, Konstantin Pogorelov, N. Conci, P. Halvorsen, F. D. Natale","doi":"10.1145/3095713.3095726","DOIUrl":"https://doi.org/10.1145/3095713.3095726","url":null,"abstract":"Gathering information, and continuously monitoring the affected areas after a natural disaster can be crucial to assess the damage, and speed up the recovery process. Satellite imagery is being considered as one of the most productive sources to monitor the after effects of a natural disaster; however, it also comes with a lot of challenges and limitations, due to slow update. It would be beneficiary to link remote sensed data with social media for the damage assessment, and obtaining detailed information about a disaster. The additional information, which are obtainable by social media, can enrich remote-sensed data, and overcome its limitations. To tackle this, we present a system called JORD that is able to autonomously collect social media data about natural disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. We also provide content based analysis along with temporal and geo-location based filtering to provide more accurate information to the users. To show the capabilities of the system, we demonstrate that a large number of disaster events can be detected by the system. In addition, we use crowdsourcing to demonstrate the quality of the provided information about the disasters, and usefulness of JORD from potential users point of view","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129641501","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: 23
Selection and Combination of Unsupervised Learning Methods for Image Retrieval 图像检索中无监督学习方法的选择与组合
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095741
Lucas Pascotti Valem, D. C. G. Pedronette
{"title":"Selection and Combination of Unsupervised Learning Methods for Image Retrieval","authors":"Lucas Pascotti Valem, D. C. G. Pedronette","doi":"10.1145/3095713.3095741","DOIUrl":"https://doi.org/10.1145/3095713.3095741","url":null,"abstract":"The evolution of technologies to store and share images has made imperative the need for methods to index and retrieve multimedia information based on visual content. The CBIR (Content-Based Image Retrieval) systems are the main solution in this scenario. Originally, these systems were solely based on the use of low-level visual features, but evolved through the years in order to incorporate various supervised learning techniques. More recently, unsupervised learning methods have been showing promising results for improving the effectiveness of retrieval results. However, given the development of different methods, a challenging task consists in to exploit the advantages of diverse approaches. As different methods present distinct results even for the same dataset and set of features, a promising approach is to combine these methods. In this work, a framework is proposed aiming at selecting the best combination of methods in a given scenario, using different strategies based on effectiveness and correlation measures. Regarding the experimental evaluation, six distinct unsupervised learning methods and two different datasets were used. The results as a whole are promising and also reveal good perspectives for future works.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"808 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123916262","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: 5
Speaker Clustering Based on Non-Negative Matrix Factorization Using Gaussian Mixture Model in Complementary Subspace 基于互补子空间高斯混合模型非负矩阵分解的说话人聚类
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095721
M. Nishida, Seiichi Yamamoto
{"title":"Speaker Clustering Based on Non-Negative Matrix Factorization Using Gaussian Mixture Model in Complementary Subspace","authors":"M. Nishida, Seiichi Yamamoto","doi":"10.1145/3095713.3095721","DOIUrl":"https://doi.org/10.1145/3095713.3095721","url":null,"abstract":"Speech feature variations are mainly attributed to variations in phonetic and speaker information included in speech data. If these two types of information are separated from each other, more robust speaker clustering can be achieved. Principal component analysis transformation can separate speaker information from phonetic information, under the assumption that a space with large within-speaker variance is a \"phonetic subspace\" and a space within-speaker variance is a \"phonetic sub-space\". We propose a speaker clustering method based on non-negative matrix factorization using a Gaussian mixture model trained in the speaker subspace. We carried out comparative experiments of the proposed method with conventional methods based on Bayesian information criterion and Gaussian mixture model in an observation space. The experimental results showed that the proposed method can achieve higher clustering accuracy than conventional methods.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957430","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: 2
Searching and annotating 100M Images with YFCC100M-HNfc6 and MI-File 使用YFCC100M-HNfc6和MI-File对100M图像进行检索和标注
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095740
Giuseppe Amato, F. Falchi, C. Gennaro, F. Rabitti
{"title":"Searching and annotating 100M Images with YFCC100M-HNfc6 and MI-File","authors":"Giuseppe Amato, F. Falchi, C. Gennaro, F. Rabitti","doi":"10.1145/3095713.3095740","DOIUrl":"https://doi.org/10.1145/3095713.3095740","url":null,"abstract":"We present an image search engine that allows searching by similarity about 100M images included in the YFCC100M dataset, and annotate query images. Image similarity search is performed using YFCC100M-HNfc6, the set of deep features we extracted from the YFCC100M dataset, which was indexed using the MI-File index for efficient similarity searching. A metadata cleaning algorithm, that uses visual and textual analysis, was used to select from the YFCC100M dataset a relevant subset of images and associated annotations, to create a training set to perform automatic textual annotation of submitted queries. The on-line image and annotation system demonstrates the effectiveness of the deep features for assessing conceptual similarity among images, the effectiveness of the metadata cleaning algorithm, to identify a relevant training set for annotation, and the efficiency and accuracy of the MI-File similarity index techniques, to search and annotate using a dataset of 100M images, with very limited computing resources.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124422879","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
On Reflection Symmetry In Natural Images 论自然图像中的反射对称性
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095743
Alessandro Gnutti, Fabrizio Guerrini, R. Leonardi
{"title":"On Reflection Symmetry In Natural Images","authors":"Alessandro Gnutti, Fabrizio Guerrini, R. Leonardi","doi":"10.1145/3095713.3095743","DOIUrl":"https://doi.org/10.1145/3095713.3095743","url":null,"abstract":"Many new symmetry detection algorithms have been recently developed, thanks to an interest revival on computational symmetry for computer graphics and computer vision applications. Notably, in 2013 the IEEE CVPR Conference organized a dedicated workshop and an accompanying symmetry detection competition. In this paper we propose an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on saliency. The symmetry value is obtained as the energy balance of the even-odd decomposition of a patch w.r.t. each possible axis. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered axis orientation. These found candidate axes are finally evaluated through a confidence measure that also allow removing redundant detected symmetries. The obtained results within the framework adopted in the aforementioned competition show significant performance improvement.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132732323","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
Building a Disclosed Lifelog Dataset: Challenges, Principles and Processes 建立公开的生活日志数据集:挑战、原则和过程
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095736
Duc-Tien Dang-Nguyen, Liting Zhou, Rashmi Gupta, M. Riegler, C. Gurrin
{"title":"Building a Disclosed Lifelog Dataset: Challenges, Principles and Processes","authors":"Duc-Tien Dang-Nguyen, Liting Zhou, Rashmi Gupta, M. Riegler, C. Gurrin","doi":"10.1145/3095713.3095736","DOIUrl":"https://doi.org/10.1145/3095713.3095736","url":null,"abstract":"In this paper, we address the challenge of how to build a disclosed lifelog dataset by proposing the principles for building and sharing such types of data. Based on the proposed principles, we describe processes for how we built the benchmarking lifelog dataset for NTCIR-13 - Lifelog 2 tasks. Further, a list of potential applications and a framework for anonymisation are proposed and discussed.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131331291","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: 23
Outdoor Scene Labeling Using ALE and LSC Superpixels 使用ALE和LSC超像素的户外场景标记
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095739
Rabia Tahir, Sheikh Ziauddin, A. R. Shahid, A. Safi
{"title":"Outdoor Scene Labeling Using ALE and LSC Superpixels","authors":"Rabia Tahir, Sheikh Ziauddin, A. R. Shahid, A. Safi","doi":"10.1145/3095713.3095739","DOIUrl":"https://doi.org/10.1145/3095713.3095739","url":null,"abstract":"Scene labeling has been an important and popular area of computer vision and image processing for the past few years. It is the process of assigning pixels to specific predefined categories in an image. A number of techniques have been proposed for scene labeling but all have some limitations regarding accuracy and computational time. Some methods only incorporate the local context of images and ignore the global information of objects in an image. Therefore, accuracy of scene labeling is low for these methods. There is a need to address these issues of scene labeling to improve labeling accuracy. In this paper, we perform outdoor scene labeling using Automatic labeling Environment (ALE). We enhance this framework by incorporating bilateral filter based preprocessing, LSC superpixels and large co-occurrence weight. Experiments on a publicly available MSRC v1 dataset showed promising results with 89.44% pixel-wise accuracy and 78.02% class-wise accuracy.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128585394","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
Dimensionality Reduction for Image Features using Deep Learning and Autoencoders 使用深度学习和自动编码器的图像特征降维
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095737
Stefan Petscharnig, M. Lux, S. Chatzichristofis
{"title":"Dimensionality Reduction for Image Features using Deep Learning and Autoencoders","authors":"Stefan Petscharnig, M. Lux, S. Chatzichristofis","doi":"10.1145/3095713.3095737","DOIUrl":"https://doi.org/10.1145/3095713.3095737","url":null,"abstract":"The field of similarity based image retrieval has experienced a game changer lately. Hand crafted image features have been vastly outperformed by machine learning based approaches. Deep learning methods are very good at finding optimal features for a domain, given enough data is available to learn from. However, hand crafted features are still means to an end in domains, where the data either is not freely available, i.e. because it violates privacy, where there are commercial concerns, or where it cannot be transmitted, i.e. due to bandwidth limitations. Moreover, we have to rely on hand crafted methods whenever neural networks cannot be trained effectively, e.g. if there is not enough training data. In this paper, we investigate a particular approach to combine hand crafted features and deep learning to (i) achieve early fusion of off the shelf handcrafted global image features and (ii) reduce the overall number of dimensions to combine both worlds. This method allows for fast image retrieval in domains, where training data is sparse.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131220028","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: 27
Lisbon Landmark Lenslet Light Field Dataset: Description and Retrieval Performance 里斯本Landmark Lenslet光场数据集:描述与检索性能
Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing Pub Date : 2017-06-19 DOI: 10.1145/3095713.3095723
J. A. Teixeira, Catarina Brites, F. Pereira, J. Ascenso
{"title":"Lisbon Landmark Lenslet Light Field Dataset: Description and Retrieval Performance","authors":"J. A. Teixeira, Catarina Brites, F. Pereira, J. Ascenso","doi":"10.1145/3095713.3095723","DOIUrl":"https://doi.org/10.1145/3095713.3095723","url":null,"abstract":"Popular local feature extraction schemes, such as SIFT, are robust when changes in illumination, translation and scale occur, and play an important role in visual content retrieval. However, these solutions are not very robust to 3D object rotations and camera viewpoint changes. In such scenarios, the emerging and richer lenslet light field image representation can provide additional information such as multiple perspectives and depth data. This paper introduces a new lenslet light field imaging dataset and studies the retrieval performance when popular 2D visual descriptors are applied. The new dataset consists of 25 Lisbon landmarks captured with a lenslet camera from different perspectives. Moreover, this paper proposes and assesses straightforward extensions of visual 2D descriptor matching for lenslet light field retrieval. The experimental results show that gains up to 14% can be obtained with a light field representation when compared to a 2D imaging conventional representation.","PeriodicalId":310224,"journal":{"name":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131264058","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
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