基于多特征和多分类融合的视频概念检测

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systematically investigated, and kernel-based learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systems on both classifier-level and kernel-level fusion that contribute to a more robust system. Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is much better than the benchmark performance, which proves that our concepts detection engine develops a generic model and performs well on both object and scene type concepts.     

基于多种类视觉特征的混合高斯背景模型

Stauffer等人提出的混合高斯背景减除建模技术及其改进算法在真实场景的运动目标检测系统中取得了较好的检测效果且被人们广泛应用。然而,此类方法通常采用单一的颜色视觉特征进行建模。当运动目标的表观颜色和背景场景的表观颜色相近时,检测准确度会大大降低。对于场景亮度条件的突变而引起的前景噪声,即使采用模型更新机制,也不能有效及时的去除。针对这些不足,提出一种基于颜色、边缘和纹理视觉特征的混合高斯建模技术。新的建模特征能够很好的描述背景区域的本质,对前景目标有着非常好的区分力,并且采用准确率和召回率对实验结果进行定量分析。实验分析表明,新算法有效地解决了传统算法存在的问题。同时也为后继的高层视觉分析任务打下了良好的基础。