Surveying for man-made objects in photographic images

Surveying for man-made objects in photographic images is of utmost importance for various military and civilian applications. In this paper, we present two supervised approaches for classifying a photographic image as containing either dominant natural or man-made regions. The first approach has low-complexity where features are extracted from a statistical model of multi-scale sub-band coefficients of natural scenes. The second approach is based on traditional robust feature extraction along with recent deep methods. We evaluate the performance of these approaches on two popular image databases composed of a mixture of man-made and natural scene photographic images. We compare their performance in terms of classification accuracy as well as computational complexity. While the traditional robust feature based classification approach appears to be an obvious choice for such a task, we conclude that low-complexity approaches cannot be discounted for real-time applications. Finally, we also construct a likelihood map for the man-made regions for quick localisation of man-made regions within mixed image that could help in speeding up the detection process.

[1]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[2]  Xiuping Jia,et al.  Crater Detection Based on Gist Features , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Martial Hebert,et al.  Man-made structure detection in natural images using a causal multiscale random field , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Tong Zhang,et al.  An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods , 2001, AI Mag..

[7]  Rajiv Soundararajan,et al.  Study of Subjective and Objective Quality Assessment of Video , 2010, IEEE Transactions on Image Processing.

[8]  Anil K. Jain,et al.  On image classification: city images vs. landscapes , 1998, Pattern Recognit..

[9]  Sameer Singh,et al.  Natural object classification using artificial neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[10]  Jorma Laaksonen,et al.  Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Bertrand Le Saux,et al.  Boosting for interactive man-made structure classification , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Jake K. Aggarwal,et al.  Lower-level and higher-level approaches to content-based image retrieval , 2000, 4th IEEE Southwest Symposium on Image Analysis and Interpretation.

[13]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[14]  Stephen Gould,et al.  Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  Jianwei Ma,et al.  Man-made Object Detection Based on Texture Visual Perception , 2012 .

[17]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  C. N. Canagarajah,et al.  Statistical Image Fusion with Generalised Gaussian and Alpha-Stable Distributions , 2007, 2007 15th International Conference on Digital Signal Processing.

[19]  Jake K. Aggarwal,et al.  Applying perceptual grouping to content-based image retrieval: building images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  Md. Abdul Alim Sheikh.  A novel self-assessed approach for classification of manmade objects and natural scene images from aerial images , 2011, 2011 Annual IEEE India Conference.

[22]  Detection of man-made-objects based on spatial aggregations , 2005 .

[23]  Charles A. Bouman,et al.  A multiscale random field model for Bayesian image segmentation , 1994, IEEE Trans. Image Process..

[24]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[25]  Martin J. Wainwright,et al.  Scale Mixtures of Gaussians and the Statistics of Natural Images , 1999, NIPS.

[26]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Roberto Cipolla,et al.  Semantic object classes in video: A high-definition ground truth database , 2009, Pattern Recognit. Lett..

[28]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.