DLANet: A manifold-learning-based discriminative feature learning network for scene classification
Feng Ziyong Z; Jin Lianwen; Tao Dapeng; Huang Shuangping
刊名NEUROCOMPUTING
2015
英文摘要This paper presents Discriminative Locality Alignment Network (DLANet), a novel manifold-learning-based discriminative learnable feature, for wild scene classification. Based on a convolutional structure, DLANet learns the filters of multiple layers by applying DLA and exploits the block-wise histograms of the binary codes of feature maps to generate the local descriptors. A DLA layer maximizes the margin between the inter-class patches and minimizes the distance of the intra-class patches in the local region. In particular, we construct a two-layer DLANet by stacking two DLA layers and a feature layer. It is followed by a popular framework of scene classification, which combines Locality-constrained Linear Coding-Spatial Pyramid Matching (LLC-SPM) and linear Support Vector Machine (SVM). We evaluate DLANet on NYU Depth V1, Scene-15 and MIT Indoor-67. Experiments show that DLANet performs well on depth image. It outperforms the carefully tuned features, including SIFT and is also competitive to the other reported methods. (C) 2015 Elsevier B.V. All rights reserved
收录类别SCI
原文出处http://www.sciencedirect.com/science/article/pii/S0925231215000880
语种英语
内容类型期刊论文
源URL[http://ir.siat.ac.cn:8080/handle/172644/6681]  
专题深圳先进技术研究院_集成所
作者单位NEUROCOMPUTING
推荐引用方式
GB/T 7714
Feng Ziyong Z,Jin Lianwen,Tao Dapeng,et al. DLANet: A manifold-learning-based discriminative feature learning network for scene classification[J]. NEUROCOMPUTING,2015.
APA Feng Ziyong Z,Jin Lianwen,Tao Dapeng,&Huang Shuangping.(2015).DLANet: A manifold-learning-based discriminative feature learning network for scene classification.NEUROCOMPUTING.
MLA Feng Ziyong Z,et al."DLANet: A manifold-learning-based discriminative feature learning network for scene classification".NEUROCOMPUTING (2015).
个性服务
查看访问统计
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。


©版权所有 ©2017 CSpace - Powered by CSpace