基于视觉敏感区域信息增强的室内场景分类算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201706005

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Abstract In the indoor scene classification, the classification accuracy is affected by various interference factors caused by the complexity and diversity of the scene structure itself. Aiming at these problems, an indoor scene classification algorithm based on the information enhancement of visual sensitive area is proposed in this paper. By fusing the local features and the global features based on the visual sensitive region information, the multi-scale space-frequency fusion feature is constructed to classify the indoor scenes correctly. Experimental results on 3 testing sets show that the proposed algorithm obtains good classification results on different scene classification datasets with strong applicability.

Key words： Indoor Scene Sensitive Area Detection Multi-scale Space-Frequency Fusion Feature Sensitive Information Enhancement

Received: 20 January 2017

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61673318,61502385), Xi'an Science and Technology Planned
Project(No.CXY1509(13))

About author:: (SHI Jing, born in 1983, Ph. D. candidate, lecturer. Her research interests include scene image classification, pattern recognition and digital image processing.)
(ZHU Hong(Corresponding author), born in 1963, Ph. D., professor. Her research interests include digital image processing, intelligent video surveillance and pattern recognition.)
(WANG Jing, born in 1985, Ph. D. candidate. Her research interests include pattern recognition and digital image processing.)
(XUE Shan, born in 1988, Ph. D. candidate. His research interests include pattern recognition and digital image processing.)

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	SHI Jing
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Cite this article:

SHI Jing,ZHU Hong,WANG Jing等. Indoor Scene Classification Algorithm Based on Information Enhancement of Vision Sensitive Area[J]. , 2017, 30(6): 520-529.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201706005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I6/520

[1] ZOU J Y, LI W, CHEN C, et al. Scene Classification Using Local and Global Features with Collaborative Representation Fusion. Information Sciences, 2016, 348: 209-226.
[2] LU G Y, YAN Y, REN L, et al. Where Am I in the Dark: Exploring Active Transfer Learning on the Use of Indoor Localization Based on Thermal Imaging. Neurocomputing, 2016, 173: 83-92.
[3] ZUO Z, WANG G, SHUAI B, et al. Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification. Pattern Recognition, 2015, 48(10): 3004-3015.
[4] 罗会兰,郭敏杰,孔繁胜.集成多特征与稀疏编码的图像分类方法.模式识别与人工智能, 2014, 27(4): 345-355.
(LUO H L, GUO M J, KONG F S. Image Classification Method by Combining Multi-features and Sparse Coding. Pattern Recognition and Artificial Intelligence, 2014, 27(4): 345-355.)
[5] KIM J H, CHOI B J, CHUN S W, et al. The Target Detection and Classification Method Using SURF Feature Points and Image Displacement in Infrared Images. Journal of the Korea Society of Computer and Information, 2014, 19(11): 43-52.
[6] XUE Z Y, RAHMAN M M, ANTANI S K, et al. Modality Classification for Searching Figures in Biomedical Literature // Proc of the 29th IEEE International Symposium on Computer-Based Medical Systems. Washington, USA: IEEE, 2016: 152-157.
[7] PARK S, KAABOUCH N, FEVIG R, et al. Scene Classification and Landmark Spotting in Multilayer Image Mosaic for UAVs in GPS-denied Situations[C/OL].[2016-12-25]. http://toc.procee
dings.com/15534webtoc.pdf.
[8] MAGGIORI E,TARABALKA Y,CHARPIAT G,et al.Convolutional Neural Networks for Large-Scale Remote-Sensing Image Cla-
ssification. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 645-657.
[9] JEONG D J, YOO H J, CHO N I. Consumer Video Summarization Based on Image Quality and Representativeness Measure // Proc of the IEEE Global Conference on Signal and Information Processing. Washington, USA: IEEE, 2015: 572-576.
[10] BARGAL S A, BARSOUM E, FERRER C C, et al. Emotion Re-
cognition in the Wild from Videos Using Images // Proc of the 18th ACM International Conference on Multimodal Interaction. New York, USA: ACM, 2016: 433-436.
[11] WANG F, ZHOU L, CUI Z Q, et al. Gesture Recognition Based onBoFand Its Application in Human-Machine Interaction of Ser-
vice Robot // Proc of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Washington, USA: IEEE, 2016: 115-120.
[12] BOSCH A, MUOZ X, MARTI R. Which Is the Best Way to Organize/Classify Images by Content? Image and Vision Computing, 2007, 25(6): 778-791.
[13] ZHOU L, ZHOU Z T, HU D W. Scene Classification Using Multi-resolution Low-Level Feature Combination. Neurocomputing, 2013, 122: 284-297.
[14] WU J X, REHG J M. CENTRIST: A Visual Descriptor for Scene Categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1489-1501.
[15] SINGH S, GUPTA A, EFROS A A. Unsupervised Discovery of Mid-level Discriminative Patches // Proc of the 12th European Conference on Computer Vision. Berlin, Germany: Springer, 2012, II: 73-86.
[16] LI L Y, GOH W X, LIM J H, et al. Extended Spectral Regression for Efficient Scene Recognition. Pattern Recognition, 2014, 47(9): 2940-2951.
[17] 江悦,王润生,王程.采用上下文金字塔特征的场景分类.计算机辅助设计与图形学学报, 2010, 22(8): 1366-1373.
(JIANG Y, WANG R S, WANG C. Scene Classification with Context Pyramid Features. Journal of Computer-Aided Design and Computer Graphics, 2010, 22(8): 1366-1373.)
[18] WU R, YE Z P, LIU P, et al. Knowledge as Action: A Cognitive Framework for Indoor Scene Classification // Proc of the IEEE International Conference on Image Processing. Washington, USA: IEEE, 2015: 3141-3144.
[19] GUPTA S, GIRSHICK R, ARBELEZ P, et al. Learning Rich Features from RGB-D Images for Object Detection and Segmentation // Proc of the 13th European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 345-360.
[20] GUPTA S, ARBELAEZ P, MALIK J. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 564-571.
[21] GUPTA S, ARBELEZ P, GIRSHICK R, et al. Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation. International Journal of Computer Vision, 2015, 112(2): 133-149.
[22] ZHANG L, FANG B, ZHAO X, et al. Pointer-Type Meter Automatic Reading from Complex Environment Based on Visual Saliency // Proc of the International Conference on Wavelet Analysis and Pattern Recognition. Washington, USA: IEEE, 2016: 264-269.
[23] CORNIA M, BARALDI L, SERRA G, et al. Multi-level Net: A Visual Saliency Prediction Model // Proc of the 14th European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 302-315.
[24] LU J Y, WEN X M, SHAO H, et al. An Effective Visual Saliency Detection Method Based on Maximum Entropy Random Walk // Proc of the IEEE International Conference on Multimedia & Expo Workshops. Washington, USA: IEEE, 2016. DOI: 10.1109/ICMEW.2016.7574714.
[25] GOFERMAN S, ZELNIK-MANOR L, TAL A. Context-Aware Saliency Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915-1926.
[26] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
[27] 史静,朱虹,邢楠,等.一种多尺度时频纹理特征融合的场景分类算法.仪器仪表学报, 2016, 37(10): 2333-2339.
(SHI J, ZHU H, XING N, et al. Multi-scale Time-Frequency Texture Feature Fusion Algorithm for Scene Classification. Chinese Journal of Scientific Instrument, 2016, 37(10): 2333-2339.)
[28] LOWE D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110.
[29] QUATTONI A, TORRALBA A. Recognizing Indoor Scenes // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 413-420.
[30] LI L J, LI F F. What, Where and Who? Classifying Events by Scene and Object Recognition // Proc of the 11th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2007. DOI: 10.1109/ICCV.2007.4408872.
[31] LAZEBNIK S, SCHMID C, PONCE J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2006, II: 2169-2178.
[32] OLIVA A, TORRALBA A. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision, 2001, 42(3): 145-175.
[33] LI F F, PERONA P. A Bayesian Hierarchical Model for Learning Natural Scene Categories // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005, II: 524-531.
[34] GAO S H, TSANG I W H, CHIA L T. Kernel Sparse Representation for Image Classification and Face Recognition // Proc of the 11th European Conference on Computer Vision. Berlin, Germany: Springer, 2010: 1-14.
[35] 范玉华,秦世引.基于潜在语义分析的场景分类优化决策方法.
计算机辅助设计与图形学学报, 2013, 25(2): 175-182.(FAN Y H, QIN S Y. Optimizing Decision for Scene Classification Based on Latent Semantic Analysis. Journal of Computer-Aided Design and Computer Graphics, 2013, 25(2): 175-182.)