Abstract
In this paper, we present a novel framework for scene image classification, which depends on corresponding visual words concatenation of speeded up robust features (SURF) and Directional binary code (DBC) feature descriptor. Firstly, we use SURF feature descriptor as a local feature descriptor. The local feature descriptor captures very close visual appearance (distinct structure) among their visual contents representation of an image. Secondly, the DBC feature descriptor captures global features, where color-texture features are extracted from entire image. Then, visual words of local and global descriptors are build separately. The concatenated visual words are used to represent the training images and query image. The SVM classifier is used to classify training samples and a query image is classified based on the similarity between histograms of training samples and query image. We carried out experiments using the challenging scene datasets such as MIT scene, UIUC sports event, and MIT indoor scene datasets. The experimental results demonstrate that the proposed method outperforms compared to the existing scene image classification methods.
Similar content being viewed by others
References
Baig F, Mehmood Z, Rashid M, Javid MA, Rehman A, Saba T, Adnan A (2020) Boosting the performance of the BoVW model using SURF-CoHOG-based sparse features with relevance feedback for CBIR. Iran J Sci Technol Trans Electr Eng 44(1):99–118
Banerji S et al (2013) New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117:173–185 https://doi.org/10.1016/j.neucom.2013.02.014
Banerji S, Sinha A, Chengjun L (2013) New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117:173–185. https://doi.org/10.1016/j.neucom.2013.02.014
Bay H, Tuytelaars T, Van Gool L (2008) SURF: Speeded up robust features. Comput Vis Image Underst 110(3):346–359
Csurka G, Bray C, Dance C, Fan L (2004) Visual categorization with bags of keypoints. In: Workshop on Statistical Learning. ECCV Computer Vision, pp 1–22
Douik A, Abdellaoui M, Kabbai L (2016) Content based image retrieval using local and global features descriptor. IEEE, International Conference on Advanced Technologies for Signal and Image Processing 151–154
Fei-Fei L, Li LJ (2010) What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization. In: Cipolla R, Battiato S, Farinella GM (eds) Computer Vision. Studies in Computational Intelligence, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12848-6_6
Hassaballah M, Abdelmgeid A, Hammam, A (2010) Image features detection, description and matching. https://doi.org/10.1007/978-3-319-28854-3_2
He N, Fang L, Li S, Plara A (2018) Covariance matrix based feature fusion for scene classification 3587–3590. https://doi.org/10.1109/IGARSS.2018.8517914
Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662
Heikkila M, Pietikainen M, Schmid C (2009) Description of interest regions with local binarypatterns. Pattern Recogn 42(3):425–436
Hiremath P, Pujari J (2007) Content based image retrieval using color, texture and shape features. In Int Conf on Advanced Computing and Communications. IEEE, pp 780–784
Jalab H (2011) Image retrieval system based on color layout descriptor and Gabor filters. In: IEEE Conference on Open Systems, ICOS 2011, pp 32–36. https://doi.org/10.1109/ICOS.2011.6079266
Kabbai L, Douik A, Abdellaoui M (2016) Content based image retrieval using local and global features descriptor. international conference on advanced Technologies for Signal and Image Processing , IEEE, pp 151–154
Kabbai L, Abdellaoui M, Douik A (2019) Image classification by combining local and global features. Vis Comput 35(5):679–693
Kaljahi MA, Palaiahnakote S, Anisi MH et al (2019) A scene image classification technique for a ubiquitous visual surveillance system. Multimed Tools Appl 5791–5818. https://doi.org/10.1007/s11042-018-6151-x
Kim KI, Jung K, Park SH, Kim HJ (2002) Support vector machines for texture classification. IEEE Trans Pattern Anal Mach Intell 24:1542–1550
Li LJ, Li FF (2009) What, where and who? Classifying events by scene and object recognition. In: IEEE International Conference on Computer Vision. pp 1–8
Liu S, Tian G (2019) An Indoor Scene Classification Method for Service Robot Based on CNN Feature. J Robot 2019. https://doi.org/10.1155/2019/8591035
Ma J, Ma Z, Kang B, Lu K (2014) A method of protein model classification and retrieval using bag-of-visual-features. Comput Math Methods Med. https://doi.org/10.1155/2014/269394
Nagaraja S, Prabhakar CJ (2015) Low-level features for image retrieval based on extraction of directional binary patterns and its oriented gradients histogram. Computer Applications: An International Journal 2:13–28
Oliva A, Torralba A (2009) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vision 42(3):145–175
Qin J, Yung NHC (2010) Scene categorization via contextual visual words. Pattern Recognit 43(5):1874–1888
Quattoni A, Torralba A (2009) Recognizing indoor scenes. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 413–420
Rahman M, Rahman S, Rahman R et al (2017) DTCTH: a discriminative local pattern descriptor for image classification. J Image Video Proc. https://doi.org/10.1186/s13640-017-0178-1
Rasiwasia N, Vasconcelos N (2016) Latent Dirichlet allocation models for image classification. IEEE Trans PAMI 35(11):2665–2679
Shawe-Taylor J (2009) Kernel methods and support vector machines. Lecture Notes
Siagian C, Itti, L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312. https://doi.org/10.1109/TPAMI.2007.40
Sleit A, Abu Dalhoum A, Qatawneh M, Al-Sharief M, Al-Jabaly R, Karajeh O (2011) Image clustering using color, texture and shape features. KSII Trans Internet Inf Syst 5:211–227. https://doi.org/10.3837/tiis.2011.01.012
Thontadari C, Prabhakar CJ (2017) Bag of visual words for word spotting in handwritten documents based on curvature features. Int J Comput Sci Inf Technol Res. https://doi.org/10.5121/ijcsit.2017.9406
Tsai CF (2012) Bag-of-words representation in image annotation: A review. International Scholarly Research Notices, Artificial Intelligence
Văduva C, Gavăt I, Datcu M (2013) Latent Dirichlet allocation for spatial analysis of satellite images. IEEE Trans Geosci Remote Sens 51(5):2770–2786
Vailaya A, Figueiredo A, Jain A, Zhang H (2001) Image classification for content-based indexing. IEEE Trans Image Process 117–129
van de Sande KE, Gevers T, Snoek CG (2011) Empowering visual categorization with the GPU. IEEE Trans Multimedia 13(1):60–70
Watanabe K, Liu S, Tian G (2019). An indoor scene classification method for service robot Based on CNN feature. Journal of Robotics, 2019
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. IEEE Conf Comput Vis Pattern Recognit 1794–1801
Zhong Y, Zhu Q, Zhang L (2015) Scene classification based on the multi feature fusion probabilistic topic model for high spatial resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 1–16
Zou J, Li W, Chen C, Du Q (2016) Scene classification using local and global features with collaborative representation. Inf Sci 100(348):209–226
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shrinivasa S R, Prabhakar C J Scene image classification based on visual words concatenation of local and global features. Multimed Tools Appl 81, 1237–1256 (2022). https://doi.org/10.1007/s11042-021-11354-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11354-5