Proceedings of the 1st ACM International Conference on Multimedia Retrieval最新文献_第7页

Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval 综合训练多视点目标分类和视点检测，用于高级图像检索

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1991999

Johannes Schels, Joerg Liebelt, K. Schertler, R. Lienhart

引用次数: 15

Instant video summarization during shooting with mobile phone 手机拍摄时即时视频汇总

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992036

Xiao Zeng, Xiaohui Xie, Kongqiao Wang

引用次数: 9

NV-Tree: nearest neighbors at the billion scale nv树:十亿尺度上的近邻

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992050

Herwig Lejsek, B. Jónsson, L. Amsaleg

引用次数: 47

Exploiting contextual spaces for image re-ranking and rank aggregation 利用上下文空间进行图像重新排序和秩聚合

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992009

D. C. G. Pedronette, R. Torres

{"title":"Exploiting contextual spaces for image re-ranking and rank aggregation","authors":"D. C. G. Pedronette, R. Torres","doi":"10.1145/1991996.1992009","DOIUrl":"https://doi.org/10.1145/1991996.1992009","url":null,"abstract":"The objective of Content-based Image Retrieval (CBIR) systems is to return the most similar images given an image query. In this scenario, accurately ranking collection images is of great relevance. In general, CBIR systems consider only pairwise image analysis, that is, compute similarity measures considering only pair of images, ignoring the rich information encoded in the relations among several images. This paper presents a novel re-ranking approach based on contextual spaces aiming to improve the effectiveness of CBIR tasks, by exploring relations among images. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information. The re-ranking method can also be applied to other tasks, such as: (i) for combining ranked lists obtained by using different image descriptors (rank aggregation); and (ii) for combining post-processing methods. We conducted several experiments involving shape, color, and texture descriptors and comparisons to other post-processing methods. Experimental results demonstrate the effectiveness of our method.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"27 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":"117192496","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: 30

RetrievalLab: a programming tool for content based retrieval RetrievalLab:用于基于内容的检索的编程工具

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992067

Ard A. J. Oerlemans, M. Lew

引用次数: 0

Component-based track inspection using machine-vision technology 采用机器视觉技术的基于组件的轨道检测

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992056

Y. Li, Charles Otto, N. Haas, Yuichi Fujiki, Sharath Pankanti

{"title":"Component-based track inspection using machine-vision technology","authors":"Y. Li, Charles Otto, N. Haas, Yuichi Fujiki, Sharath Pankanti","doi":"10.1145/1991996.1992056","DOIUrl":"https://doi.org/10.1145/1991996.1992056","url":null,"abstract":"In this paper, we present our latest research engagement with a railroad company to apply machine vision technologies to automate the inspection and condition monitoring of railroad tracks. Specifically, we have proposed a complete architecture including imaging setup for capturing multiple video streams, important rail component detection such as tie plate, spike, anchor and joint bar bolt, defect identification such as raised spikes, defect severity analysis and temporal condition analysis, and long-term predictive assessment. This paper will particularly present various video analytics that we have developed to detect rail components, which form the building block of the entire framework. Our preliminary performance study has achieved an average of 98.2% detection rate, 1.57% false positive rate and 1.78% false negative rate on the component detection. Finally, with the lack of sufficient representative data and annotations to evaluate system performance on exception detection at both sequence and compliance levels, we proposed a mathematical modeling approach to calculate the probabilities of detecting such exceptions. Such analysis shows that there is still big room for us to improve our approaches in order to achieve desired false positive rate and miss detection rate at the sequence level.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"8 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":"122363190","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: 46

Indexing the signature quadratic form distance for efficient content-based multimedia retrieval 为有效的基于内容的多媒体检索索引签名二次形式距离

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992020

C. Beecks, Jakub Lokoč, T. Seidl, T. Skopal

引用次数: 37

Fusing heterogeneous modalities for video and image re-ranking 融合异构模式的视频和图像重排序

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992011

Hung-Khoon Tan, C. Ngo

引用次数: 22

Spatial codebooks for image categorization 用于图像分类的空间码本

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992046

Eugene Mbanya, S. Gerke, P. Ndjiki-Nya

引用次数: 9

Lost in binarization: query-adaptive ranking for similar image search with compact codes 在二值化中丢失:使用紧凑代码搜索相似图像的查询自适应排序

Proceedings of the 1st ACM International Conference on Multimedia Retrieval Pub Date : 2011-04-18 DOI: 10.1145/1991996.1992012

Yu-Gang Jiang, Jun Wang, Shih-Fu Chang

{"title":"Lost in binarization: query-adaptive ranking for similar image search with compact codes","authors":"Yu-Gang Jiang, Jun Wang, Shih-Fu Chang","doi":"10.1145/1991996.1992012","DOIUrl":"https://doi.org/10.1145/1991996.1992012","url":null,"abstract":"With the proliferation of images on the Web, fast search of visually similar images has attracted significant attention. State-of-the-art techniques often embed high-dimensional visual features into low-dimensional Hamming space, where search can be performed in real-time based on Hamming distance of compact binary codes. Unlike traditional metrics (e.g., Euclidean) of raw image features that produce continuous distance, the Hamming distances are discrete integer values. In practice, there are often a large number of images sharing equal Hamming distances to a query, resulting in a critical issue for image search where ranking is very important. In this paper, we propose a novel approach that facilitates query-adaptive ranking for the images with equal Hamming distance. We achieve this goal by firstly offline learning bit weights of the binary codes for a diverse set of predefined semantic concept classes. The weight learning process is formulated as a quadratic programming problem that minimizes intra-class distance while preserving interclass relationship in the original raw image feature space. Query-adaptive weights are then rapidly computed by evaluating the proximity between a query and the concept categories. With the adaptive bit weights, the returned images can be ordered by weighted Hamming distance at a finer-grained binary code level rather than at the original integer Hamming distance level. Experimental results on a Flickr image dataset show clear improvements from our query-adaptive ranking approach.","PeriodicalId":390933,"journal":{"name":"Proceedings of the 1st ACM International Conference on Multimedia Retrieval","volume":"22 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":"123535476","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: 47