Exploiting Object Semantic Cues for Multi-label Material Recognition

	Exploiting Object Semantic Cues for Multi-label Material Recognition
	Yang LX(杨凌霄); Xie XH(谢晓华)
刊名	Neurocomputing
	2016
英文摘要	Recognizing materials on an object׳s surface is important because it significantly benefits understanding quality and functionality of the concerned object. This paper focuses on a Multi-Label Material Recognition (M-LMR) problem that is to identify multiple material categories on an object from a single photograph. As a distinct task, material categorization is different from traditional vision recognition tasks such as recognition of shapes, objects, or scenes, and cannot be explained in terms of simple feature judgments. To address this problem, besides employing state-of-the-art image descriptors (e.g., image features learned by deep convolutional network) for distinguishing materials, we focus on exploiting object semantic cues to facilitate the M-LMR. Specifically, we derive a binary-SVM based framework that integrates image features with the object identity as input to judge surface material categories. We argue that the use of object information is essentially for exploiting correlations of material labels, where label correlations are very useful for facilitating a multi-label recognition problem. Experimental results shows consistent improvements of the presented method over state-of-the-arts, even though the object identity is automatically inferred.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S0925231215013454
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10165]
专题	深圳先进技术研究院_数字所
作者单位	Neurocomputing
推荐引用方式 GB/T 7714	Yang LX,Xie XH. Exploiting Object Semantic Cues for Multi-label Material Recognition[J]. Neurocomputing,2016.
APA	杨凌霄,&谢晓华.(2016).Exploiting Object Semantic Cues for Multi-label Material Recognition.Neurocomputing.
MLA	杨凌霄,et al."Exploiting Object Semantic Cues for Multi-label Material Recognition".Neurocomputing (2016).