Pairwise constraints based multiview features fusion for scene classification

	Pairwise constraints based multiview features fusion for scene classification
	Yu Jun; Tao Dacheng; Rui Yong; Cheng Jun
刊名	PATTERN RECOGNITION
	2013
英文摘要	Recently, we have witnessed a surge of interests of learning a low-dimensional subspace for scene classification. The existing methods do not perform well since they do not consider scenes' multiple features from different views in low-dimensional subspace construction. In this paper, we describe scene images by finding a group of features and explore their complementary characteristics. We consider the problem of multiview dimensionality reduction by learning a unified low-dimensional subspace to effectively fuse these features. The new proposed method takes both intraclass and interclass geometries into consideration, as a result the discriminability is effectively preserved because it takes into account neighboring samples which have different labels. Due to the semantic gap, the fusion of multiview features still cannot achieve excellent performance of scene classification in real applications. Therefore, a user labeling procedure is introduced in our approach. Initially, a query image is provided by the user, and a group of images are retrieved by a search engine. After that, users label some images in the retrieved set as relevant or irrelevant with the query. The must-links are constructed between the relevant images, and the cannot-links are built between the irrelevant images. Finally, an alternating optimization procedure is adopted to integrate the complementary nature of different views with the user labeling information, and develop a novel multiview dimensionality reduction method for scene classification. Experiments are conducted on the real-world datasets of natural scenes and indoor scenes, and the results demonstrate that the proposed method has the best performance in scene classification. In addition, the proposed method can be applied to other classification problems. The experimental results of shape classification on Caltech 256 suggest the effectiveness of our method.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S0031320312003524
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/4443]
专题	深圳先进技术研究院_集成所
作者单位	PATTERN RECOGNITION
推荐引用方式 GB/T 7714	Yu Jun,Tao Dacheng,Rui Yong,et al. Pairwise constraints based multiview features fusion for scene classification[J]. PATTERN RECOGNITION,2013.
APA	Yu Jun,Tao Dacheng,Rui Yong,&Cheng Jun.(2013).Pairwise constraints based multiview features fusion for scene classification.PATTERN RECOGNITION.
MLA	Yu Jun,et al."Pairwise constraints based multiview features fusion for scene classification".PATTERN RECOGNITION (2013).