Effective image and video mining: an overview of model-based approaches
Effective image and video mining: an overview of model-based approaches
复制标题
有效的图像和视频挖掘：基于模型的方法概述
DOI：
10.1145/1133890.1133895
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发表时间：
2005
影响因子：
7.3
通讯作者：
R. Palenichka
中科院分区：
文献类型：
--
作者：
R. Missaoui;R. Palenichka
本文致力于从图像建模方法的角度重新审视图像和视频挖掘技术，这些方法构成了这些技术的理论基础。属于图像或视频挖掘的最重要的领域是：图像知识提取、基于内容的图像检索、视频检索、视频序列分析、变化检测、模型学习以及对象识别。传统上，这些领域都是独立开发的，因此没有从一些常识性的方法中受益，这些方法可能提供最优和高效的解决方案。考虑了从图像集合或视频序列中提取知识的两种不同类型的输入数据：原始图像或图像的符号（模型）描述。简要介绍了几种基本模型，并对其进行了比较，以期找到解决图像和视频挖掘问题的有效方法。它们包括用于表示空间和时间实体（对象、场景或事件）的基于特征的模型和与对象相关的结构模型。
This paper is dedicated to revisiting image and video mining techniques from the viewpoint of image modeling approaches, which constitute the theoretical basis for these techniques. The most important areas belonging to image or video mining are: image knowledge extraction, content-based image retrieval, video retrieval, video sequence analysis, change detection, model learning, as well as object recognition. Traditionally, these areas have been developed independently, and hence have not benefited from some common sense approaches which provide potentially optimal and time-efficient solutions. Two different types of input data for knowledge extraction from an image collection or video sequences are considered: original image or symbolic (model) description of the image. Several basic models are described briefly and compared with each other in order to find effective solutions for the image and video mining problems. They include feature-based models and object-related structural models for the representation of spatial and temporal entities (objects, scenes or events).
DOI：
10.1109/34.868684
发表时间：
2000-08-01
影响因子：
23.6
作者：
Oliver, NM;Rosario, B;Pentland, AP
通讯作者：
Pentland, AP