Humans are extremely proficient at perceiving natural scenes and understanding their contents. Scene recognition in Human is the natural activity by which human can easily recognize the scene even if the scene is complex, partially occluded or blurred. In machine vision the recognition rate is less compared with human vision. To improve the recognition rate of the machine vision an efficient structural and textural based features are extracted from the image. H-Descriptor with Local Binary Pattern (LBP) [24] and H-Descriptor with Local Gradient Pattern (LGP) can effectively extract structural arrangement and textural arrangement of pixels in an image. LGP is invariant to local intensity variation so it is efficient for scene classification. LBP and LGP [23] is applied for each slices when the input image is separated into three different slices. Then Haar wavelet is applied for the input image and three different slices. The HOG is applied for each Haar wavelet transformed images to produce H-Descriptor with Local Binary Pattern and H-Descriptor with Local Gradient Pattern. Then by taking the H-Descriptor with Local Binary Pattern and H-Descriptor with Local Gradient Pattern as two independent feature channels, and combined them to arrive at a final decision using SphereSVM [22] for achieving an effective scene categorization.

H-Descriptor; Local Binary Pattern; Local Gradient Pattern; Haar wavelet; SphereSVM

[1] Freeman W.T and Roth M, "Orientation histograms for hand gesture recognition",Intl. Workshop on Automatic Face and Gesture- Recognition, IEEE Computer Society, Zurich, Switzerland, Page No(296-301), 1995.
[2] Szummer M and Picard R, "Indoor-outdoor image classification", In: IEEE Workshop on Content-based Access of Image and Video Databases, Bombay, India, Page No(42�51),1998.
[3] Schmid C, "Constructing models for content-based image retrieval", In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, Page No (39�45),2001.
[4] Oliva A and Torralba A," Modeling the shape of the scene: a holistic representation of the spatial envelope", IJCV 42(3),PageNo (145�175),2001.
[5] Porwik P and Lisowska A, "The haar wavelet transform in digital image processing: its status and achievement", Mach. Graph. Vis. 13, Page No (79�98),2004.
[6] Koch C, Li Fi and Van Rullen R, "Why does natural scene categorization require little nattention? Exploring attentional requirements for natural and synthetic stimuli", Visual Cognition, Page No (893�924), 2005.
[7] Lazebnik S, Ponce J and Schmid C, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories", In: IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, Page No (2169�2178),2005.
[8] Dalal N and TriggsB,"Histograms of oriented gradients for human detection", In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, Page No (886-893), 2005.
[9] Sun N, Zheng W, Sun C, Zou L, and Zhao C, "Gender Classification Based on Boosting Local Binary Pattern", Proc. Int�l Symp. Neural Networks, Page No (194-201), 2006.
[10] Oliva A and TorralbaA,"The role of context in object recognition", Trends in Cognitive Sciences, 11(12), Page No (520�527), 2007.
[11] Schiele B and Vogel J," Semantic model of natural scenes for content-based image retrieval", International Journal for Computer Vision, Page No (133�157), 2007.
[12] Zhang J, Marszalek M, Lazebnik S, "Local features and kernels for classification of texture and object categories:a comprehensive study", International Journal for Computer Vision, Page No (213�238),2007.
[13] Huang K.Q, Huang Y.Z and Tao D.C,"Enhanced biologically inspired model", In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, Page No (1�8),2008.
[14] Bosch A, Munoz X and ZissermanA,"Scene classification using a hybrid generative/discriminative approach", IEEE Transaction on Pattern Analysis Machine Intelligence, Page No (712�727), 2008.
[15] X. Wang, T.X. Han, and S. Yan, "An HOG-LBP Human Detector with Partial Occlusion Handling", Proc. DAGM Symp. Pattern Recognition, Page No( 82-91),2008.
[16] H. Bay, A. Ess, T. Tuytelaars, and L. Gool, "Surf: Speeded Up Robust Features", Computer Vision and Image Understanding, vol. 110, no. 3, Page No (346-359),2008.
[17] Beck M,Caddigan E, Fei-Fei, Walther D," Natural scene categories revealed in distributed patterns of activity in the human brain", Journal of neuroscience, 29, Page No (73-81),2008.
[18] Hebert,Pantofaru C and Schmid C , " Object recognition by integrating multiple image segmentations", In: Proceedings of the European Conference on Computer Vision, Morseille, France, Page No (481�494),2009.
[19] Verma A, Banerji S and Liu C , "A new color SIFT descriptor and methods for image category classification", in: Proceedings of the International Congress on Computer Applications and Computational Science, Singapore, Page No (819�822),2010.
[20] Tanveer Syeda-Mahmood, David Beymer, and Fei Wang, "Shape-based Matching of ECG Recording", (IJCSE) International Journal on Computer Science and Engineering Vol.02, No.07,Page No (2502-2505),2010.
[21] Hu DeWen,Zhou Li and Zhou ZongTan ,"Scene recognition combining structural and textural features", In proceedings of the National University of Defense Technology, China, Science china, Page No (1-14),2012.
[22] BeataStrack, Qi Li,RobertStrack and Vojislav Kecman," Sphere Support Vector Machines for large classification tasks", Neurocomputing, Elsevier. Vol 101, Page No (59-67), 2013.
[23] BongjinJun, DaijinKim and Inho Choi , " Local Transform Features and Hybridization for Accurate Face and Human Detection", IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 35, No. 6, Page No (1423-1436),2013.
[24] Atreyee Sinha, ChengjunLiu and SugataBanerji, "New image descriptors based on color, texture, shape, and wavelets for object and scene image classification", Neurocomputing, Elsevier. Vol 117, Page No (173-185), 2013.