A Review on Computer Vision - Scene Classification Techniques

In today's era, need for automatic response of machines on certain task has been prevalent. Humans want their life easier and automatic in every possible way. However, those tasks require better understanding by the machine to perform human like tasks. Tasks like classification, detection and localization are on high demand and dominant research area. These tasks fall into a domain called computer vision where computers by analyzing and understanding performs human like tasks. This domain provides the automatic inference by machines to make human life easier. In this paper, we focus on one of the difficult computer vision tasks called scene classification. Scene Classification deals with techniques that make machine intelligent and automated by processing given input say image. As machines are made automatic and intelligent to perform various tasks, Artificial Intelligence and Image processing comes into the picture. We study and analyze various approaches and methods by which such task can be handled easily and accurately. Furthermore, we compare all the approaches and find out the best approach to opt for this task.

[1]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Qin Yan,et al.  Scene classification with improved AlexNet model , 2017, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[4]  Anca L. Ralescu,et al.  Image Understanding - a Brief Review of Scene Classification and Recognition , 2017, MAICS.

[5]  Yangzihao Wang,et al.  Scene Classification with Deep Convolutional Neural Networks , 2014 .

[6]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Yoshua Bengio,et al.  ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks , 2015, ArXiv.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Jitendra Malik,et al.  When is scene identification just texture recognition? , 2004, Vision Research.

[10]  Hao Su,et al.  Objects as Attributes for Scene Classification , 2010, ECCV Workshops.

[11]  Siddharth Swarup Rautaray,et al.  Application of Deep Learning for Object Detection , 2018 .

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[14]  Xiaodong Mu,et al.  Scene classification of remote sensing image based on deep network grading transferring , 2018, Optik.

[15]  Rosalind W. Picard,et al.  Texture orientation for sorting photos "at a glance" , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[16]  Bolei Zhou,et al.  Places: An Image Database for Deep Scene Understanding , 2016, ArXiv.

[17]  Limin Wang,et al.  Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs , 2016, IEEE Transactions on Image Processing.

[18]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Jiebo Luo,et al.  Probabilistic spatial context models for scene content understanding , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Wei Gong,et al.  Application of deep learning in object detection , 2017, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).

[21]  Luis Herranz,et al.  Scene Recognition with CNNs: Objects, Scales and Dataset Bias , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  J. King.  Scene classification with Convolutional Neural Networks , 2017 .