语义视频搜索

A. Smeulders, J. V. Van Gemert, B. Huumink, D. Koelma, O. de Rooij, K. V. D. Van De Sande, C. Snoek, C. Veenman, M. Worring
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引用次数: 4

摘要

在本文中,我们描述了MediaMill系统在TRECVID 2006视频搜索引擎基准测试中的当前性能。MediaMill团队参与了两个任务:概念检测和搜索。对于概念检测,我们使用MediaMill Challenge作为实验平台。MediaMill挑战将通用视频索引问题分为纯视觉、纯文本、早期融合、后期融合和组合分析实验。我们为每个实验提供了一个基线实现以及基线结果。我们在全局、区域和关键点层面提取图像特征,并将其与各种监督学习器相结合。使用几何平均值的视觉分析方法的后期融合方法是我们最成功的运行。通过这次跑步,我们以超过50%的优势战胜了挑战基线。我们的概念检测实验得出了三个概念的最高分:即沙漠,旗帜我们和图表。更重要的是,使用LSCOM注释,我们的纯视觉方法可以很好地推广到491个概念检测器。为了处理如此庞大的检索词库,开发了一个引擎,允许用户基于高级可视化的交互式浏览选择相关的概念检测器。与前几年类似,我们的最佳互动搜索运行表现最佳,排名第二和第六。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Video Search
In this paper we describe the current performance of our MediaMill system as presented in the TRECVID 2006 benchmark for video search engines. The MediaMill team participated in two tasks: concept detection and search. For concept detection we use the MediaMill Challenge as experimental platform. The MediaMill Challenge divides the generic video indexing problem into a visual-only, textual- only, early fusion, late fusion, and combined analysis experiment. We provide a baseline implementation for each experiment together with baseline results. We extract image features, on global, regional, and keypoint level, which we combine with various supervised learners. A late fusion approach of visual-only analysis methods using geometric mean was our most successful run. With this run we conquer the Challenge baseline by more than 50%. Our concept detection experiments have resulted in the best score for three concepts: i.e. desert, flag us, and charts. What is more, using LSCOM annotations, our visual-only approach generalizes well to a set of 491 concept detectors. To handle such a large thesaurus in retrieval, an engine is developed which allows users to select relevant concept detectors based on interactive browsing using advanced visualizations. Similar to previous years our best interactive search runs yield top performance, ranking 2nd and 6th overall.
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