搜索结果: 1-7 共查到“计算机应用 Manifold”相关记录7条 . 查询时间(0.039 秒)
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we ...
Real-Time image Annotation by Manifold-based Biased Fisher Discriminant Analysis
Real-Time image Annotation Manifold-based
2008/12/31
Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval.However, two crucial issues are not well addressed in state-of-art annotation algori...
Cross-Media Manifold Learning for Image Retrieval & Annotation
Automatic image annotation Manifold learning Content-based image retrieval Web image search Co-training
2008/10/17
Fusion of visual content with textual information is an effective way for both content-based and keyword-based image retrieval. However, the performance of visual & textual fusion is affected greatly ...
Manifold-Manifold Distance with Application to Face Recognition based on Image Set
Manifold-Manifold Distance Application Face Recognition based Image Set
2008/6/17
In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. W...
Real-time image annotation by manifold-based biased Fisher discriminant analysis
Image Annotation Manifold Co-Training Fisher Discriminant Analysis Real-Time Annotation
2008/1/17
Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algor...
Face Detection Based on the Example Resampling by Manifold
Face Detection the Example Resampling
2007/12/31
As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face
detector is a qui...
Abstract. Data collection for both training and testing a classifier is a tedious
but essential step towards face detection and recognition. It is a piece of cake to
collect more than hundreds of th...