Proceedings of the 1st ACM International Conference on Multimedia Retrieval最新文献_第5页

Attribute-based vehicle search in crowded surveillance videos 拥挤监控视频中基于属性的车辆搜索

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992014

R. Feris, Behjat Siddiquie, Y. Zhai, James Petterson, L. Brown, Sharath Pankanti

{"title":"Attribute-based vehicle search in crowded surveillance videos","authors":"R. Feris, Behjat Siddiquie, Y. Zhai, James Petterson, L. Brown, Sharath Pankanti","doi":"10.1145/1991996.1992014","DOIUrl":"https://doi.org/10.1145/1991996.1992014","url":null,"abstract":"We present a novel application for searching for vehicles in surveillance videos based on semantic attributes. At the interface, the user specifies a set of vehicle characteristics (such as color, direction of travel, speed, length, height, etc.) and the system automatically retrieves video events that match the provided description. A key differentiating aspect of our system is the ability to handle challenging urban conditions such as high volumes of activity and environmental factors. This is achieved through a novel multi-view vehicle detection approach which relies on what we call motionlet classifiers, i.e. classifiers that are learned with vehicle samples clustered in the motion configuration space. We employ massively parallel feature selection to learn compact and accurate motionlet detectors. Moreover, in order to deal with different vehicle types (buses, trucks, SUVs, cars), we learn the motionlet detectors in a shape-free appearance space, where all training samples are resized to the same aspect ratio, and then during test time the aspect ratio of the sliding window is changed to allow the detection of different vehicle types. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as \"Show me all blue trucks larger than 7ft length traveling at high speed northbound last Saturday, from 2pm to 5pm\".","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126487775","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: 45

Adaptive clustering and interactive visualizations to support the selection of video clips 自适应聚类和交互式可视化，支持视频剪辑的选择

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992030

Andreas Girgensohn, F. Shipman, L. Wilcox

{"title":"Adaptive clustering and interactive visualizations to support the selection of video clips","authors":"Andreas Girgensohn, F. Shipman, L. Wilcox","doi":"10.1145/1991996.1992030","DOIUrl":"https://doi.org/10.1145/1991996.1992030","url":null,"abstract":"Although people are capturing more video with their mobile phones, digital cameras, and other devices, they rarely watch all that video. More commonly, users extract a still image from the video to print or a short clip to share with others. We created a novel interface for browsing through a video keyframe hierarchy to find frames or clips. The interface is shown to be more efficient than scrolling linearly through all keyframes. We developed algorithms for selecting quality keyframes and for clustering keyframes hierarchically. At each level of the hierarchy, a single representative keyframe from each cluster is shown. Users can drill down into the most promising cluster and view representative keyframes for the sub-clusters. Our clustering algorithms optimize for short navigation paths to the desired keyframe. A single keyframe is located using a non-temporal clustering algorithm. A video clip is located using one of two temporal clustering algorithms. We evaluated the clustering algorithms using a simulated search task. User feedback provided us with valuable suggestions for improvements to our system.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906364","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

Locally regressive G-optimal design for image retrieval 图像检索的局部回归g -最优设计

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992055

Zhengjun Zha, Yantao Zheng, Meng Wang, Fei Chang, Tat-Seng Chua

{"title":"Locally regressive G-optimal design for image retrieval","authors":"Zhengjun Zha, Yantao Zheng, Meng Wang, Fei Chang, Tat-Seng Chua","doi":"10.1145/1991996.1992055","DOIUrl":"https://doi.org/10.1145/1991996.1992055","url":null,"abstract":"Content Based Image Retrieval (CBIR) has attracted increasing attention from both academia and industry. Relevance Feedback is one of the most effective techniques to bridge the semantic gap in CBIR. One of the key research problems related to relevance feedback is how to select the most informative images for users to label. In this paper, we propose a novel active learning algorithm, called Locally Regressive G-Optimal Design (LRGOD) for relevance feedback image retrieval. Our assumption is that for each image, its label can be well estimated based on its neighbors via a locally regressive function. LRGOD algorithm is developed based on a locally regressive least squares model which makes use of the labeled and unlabeled images, as well as simultaneously exploits the local structure of each image. The images that can minimize the maximum prediction variance are selected as the most informative ones. We evaluated the proposed LRGOD approach on two real-world image corpus: Corel and NUS-WIDE-OBJECT [5] datasets, and compare it to three state-of-the-art active learning methods. The experimental results demonstrate the effectiveness of the proposed approach.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126974491","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

A flexible environment for multimedia management and publishing 灵活的多媒体管理和发布环境

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992072

M. Bertini, A. Bimbo, G. Ioannidis, Alexandru Stan, Emile Bijk

引用次数: 1

An eye-tracking-based approach to facilitate interactive video search 基于眼动追踪的交互式视频搜索方法

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992039

S. Vrochidis, I. Patras, Y. Kompatsiaris

引用次数: 14

A kernel density based approach for large scale image retrieval 基于核密度的大规模图像检索方法

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992024

Wei Tong, Fengjie Li, Tianbao Yang, Rong Jin, Anil K. Jain

{"title":"A kernel density based approach for large scale image retrieval","authors":"Wei Tong, Fengjie Li, Tianbao Yang, Rong Jin, Anil K. Jain","doi":"10.1145/1991996.1992024","DOIUrl":"https://doi.org/10.1145/1991996.1992024","url":null,"abstract":"Local image features, such as SIFT descriptors, have been shown to be effective for content-based image retrieval (CBIR). In order to achieve efficient image retrieval using local features, most existing approaches represent an image by a bag-of-words model in which every local feature is quantized into a visual word. Given the bag-of-words representation for images, a text search engine is then used to efficiently find the matched images for a given query. The main drawback with these approaches is that the two key steps, i.e., key point quantization and image matching, are separated, leading to sub-optimal performance in image retrieval. In this work, we present a statistical framework for large-scale image retrieval that unifies key point quantization and image matching by introducing kernel density function. The key ideas of the proposed framework are (a) each image is represented by a kernel density function from which the observed key points are sampled, and (b) the similarity of a gallery image to a query image is estimated as the likelihood of generating the key points in the query image by the kernel density function of the gallery image. We present efficient algorithms for kernel density estimation as well as for effective image matching. Experiments with large-scale image retrieval confirm that the proposed method is not only more effective but also more efficient than the state-of-the-art approaches in identifying visually similar images for given queries from large image databases.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115162866","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: 7

Active learning through notes data in Flickr: an effortless training data acquisition approach for object localization 通过Flickr中的笔记数据进行主动学习:一种轻松的目标定位训练数据获取方法

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992042

Lei Zhang, Jun Ma, C. Cui, Piji Li

引用次数: 7

Scene-based image retrieval by transitive matching 基于传递匹配的场景图像检索

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992043

A. Ulges, Christian Schulze

引用次数: 3

Consistent visual words mining with adaptive sampling 基于自适应采样的一致性视觉词挖掘

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992045

Pierre Letessier, Olivier Buisson, A. Joly

引用次数: 12

Lookapp: interactive construction of web-based concept detectors Lookapp:基于web的概念检测器的交互式构建

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992062

Damian Borth, A. Ulges, T. Breuel

引用次数: 5