{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:05:41Z","timestamp":1779217541984,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T00:00:00Z","timestamp":1594339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.<\/jats:p>","DOI":"10.3390\/s20143871","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"3871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression"],"prefix":"10.3390","volume":"20","author":[{"given":"Abrar","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Air University, E-9, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Jalal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Air University, E-9, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-9600","authenticated-orcid":false,"given":"Kibum","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gu, Y., Yan, F., and Zhuang, Y. (2019). Outdoor Scene Understanding Based on Multi-Scale PBA Image Features and Point Cloud Features. Sensors, 19.","DOI":"10.3390\/s19204546"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, C., Li, S., Fu, X., Ren, Y., Chen, Y., and Kuo, C.C.J. (2017, January 12). Exploring confusing scene classes for the places dataset: Insights and solutions. Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kuala Lumpur, Malaysia.","DOI":"10.1109\/APSIPA.2017.8282094"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, L., Cui, X., Li, Z., Yuan, Z., Xing, J., Xing, X., and Jia, Z. (2019). A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration. Sensors, 19.","DOI":"10.3390\/s19112479"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, C., Ren, Y., and Kuo, C.C.J. (2016). Outdoor scene classification using labeled segments. In Big Visual Data Analysis, Springer.","DOI":"10.1007\/978-981-10-0631-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1049\/trit.2019.0002","article-title":"New shape descriptor in the context of edge continuity","volume":"4","author":"Susan","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, C., Ren, Y., and Kuo, C.C.J. (2014, January 1). Large-scale indoor\/outdoor image classification via expert decision fusion (edf). Proceedings of the Asian Conference on Computer Vision, Los Angeles, CA, USA.","DOI":"10.1007\/978-3-319-16628-5_31"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1109\/TNNLS.2017.2757497","article-title":"Object categorization using class-specific representations","volume":"29","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Neu. Net. Learn. Sys."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rafique, A.A., Jalal, A., and Ahmed, A. (2019, January 27). Scene Understanding and Recognition: Statistical Segmented Model using Geometrical Features and Gaussian Na\u00efve Bayes. Proceedings of the IEEE conference on International Conference on Applied and Engineering Mathematics, Texila, Pakistan.","DOI":"10.1109\/ICAEM.2019.8853721"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11735","DOI":"10.3390\/s140711735","article-title":"A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments","volume":"14","author":"Jalal","year":"2014","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1504\/IJHM.2019.104386","article-title":"A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure","volume":"4","author":"Shokri","year":"2019","journal-title":"Int. J. Hydromechatron."},{"key":"ref_11","first-page":"46","article-title":"Survey over image thresholding techniques and quantitative performance evaluation","volume":"13","author":"Sezgin","year":"2004","journal-title":"J. Elect. Imaging"},{"key":"ref_12","first-page":"97","article-title":"MRI brain image segmentation based on thresholding","volume":"3","author":"Sujji","year":"2013","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_13","first-page":"9587","article-title":"Human segmentation in a complex situation based on properties of the human visual system","volume":"2","author":"Bi","year":"2006","journal-title":"Intell. Control Autom."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yan, M., Cai, J., Gao, J., and Luo, L. (2012, January 16). K-means cluster algorithm based on color image enhancement for cell segmentation. Proceedings of the 5th International Conference on BioMedical Engineering and Informatics, Chongqing, China.","DOI":"10.1109\/BMEI.2012.6513157"},{"key":"ref_15","first-page":"103","article-title":"Image segmentation and region growing algorithm","volume":"2","author":"Kamdi","year":"2012","journal-title":"Int. J. Comput. Tecnol. Elect. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4669","DOI":"10.1109\/TIP.2017.2696744","article-title":"Track everything: Limiting prior knowledge in online multi-object recognition","volume":"26","author":"Wong","year":"2017","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/TGRS.2019.2894425","article-title":"Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery","volume":"57","author":"Sumbul","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Martin, S. (2011, January 5). Sequential bayesian inference models for multiple object classification. Proceedings of the 14th International Conference on Information Fusion, Chicago, IL, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lecumberry, F., Pardo, A., and Sapiro, G. (2009, January 7\u201310). Multiple shape models for simultaneous object classification and segmentation. Proceedings of the 16th IEEE International Conference on Image Processing, Cairo, Egypt.","DOI":"10.1109\/ICIP.2009.5414596"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patcog.2016.08.003","article-title":"Robust human activity recognition from depth video using spatiotemporal multi-fused features","volume":"61","author":"Jalal","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45230","DOI":"10.1109\/ACCESS.2019.2908448","article-title":"Scene Categorization Model Using Deep Visually Sensitive Features","volume":"7","author":"Shi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"3834","article-title":"Multiview, Few-Labeled Object Categorization by Predicting Labels with View Consistency","volume":"49","author":"Zhang","year":"2019","journal-title":"IEEE Trans."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.patcog.2012.07.017","article-title":"Scene classification using a multi-resolution bag-of-features model","volume":"46","author":"Zhou","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1109\/TIP.2016.2599292","article-title":"A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification","volume":"25","author":"Hayat","year":"2016","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2016.02.021","article-title":"Scene classification using local and global features with collaborative representation fusion","volume":"348","author":"Zou","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ismail, A.S., Seifelnasr, M.M., and Guo, H. (2018, January 28). Understanding Indoor Scene: Spatial Layout Estimation, Scene Classification, and Object Detection. Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing, Shenzhen, China.","DOI":"10.1145\/3220162.3220182"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1049\/trit.2019.0017","article-title":"Three-stage network for age estimation","volume":"4","author":"Tingting","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mahajan, S.M., and Dubey, Y.K. (2015, January 4\u20136). Color image segmentation using kernalized fuzzy c-means clustering. Proceedings of the 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India.","DOI":"10.1109\/CSNT.2015.200"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1049\/trit.2019.0036","article-title":"Influence of kernel clustering on an RBFN","volume":"4","author":"Miao","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gandhi, N.J., Shah, V.J., and Kshirsagar, R. (2014, January 3\u20135). Mean shift technique for image segmentation and Modified Canny Edge Detection Algorithm for circle detection. Proceedings of the 2014 International Conference on Communication and Signal Processing, Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2014.6949838"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1504\/IJHM.2019.098949","article-title":"Engine speed reduction for hydraulic machinery using predictive algorithms","volume":"1","author":"Wiens","year":"2019","journal-title":"Int. J. Hydromechatron."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Durand, T., Picard, D., Thome, N., and Cord, M. (2014, January 27\u201330). Semantic pooling for image categorization using multiple kernel learning. Proceedings of the 2014 IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7025033"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1504\/IJHM.2019.098951","article-title":"Analytical analysis of single-stage pressure relief valves","volume":"2","author":"Weber","year":"2019","journal-title":"Int. J. Hydromechatron."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nowozin, S. (2014, January 23\u201328). Optimal decisions from probabilistic models: The intersection-over-union case. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.77"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Behadada, O., Trovati, M., Chikh, M.A., Bessis, N., and Korkontzelos, Y. (2016, January 12\u201316). Logistic regression multinomial for arrhythmia detection. Proceedings of the 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), Augsburg, Germany.","DOI":"10.1109\/FAS-W.2016.39"},{"key":"ref_37","unstructured":"Shotton, J., Winn, J., Rother, C., and Criminisi, A. (2016, January 7\u201313). Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Proceedings of the European Conference on Computer Vision, Graz, Austria."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1016\/j.patcog.2015.02.005","article-title":"Content-based image retrieval using computational visual attention model, Pattern Recognition","volume":"48","author":"Liu","year":"2015","journal-title":"Pattern Rec."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Quattoni, A., and Torralba, A. (2009, January 20). Recognizing indoor scenes. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Irie, G., Liu, D., Li, Z., and Chang, S.F. (2013, January 23\u201328). A bayesian approach to multimodal visual dictionary learning. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.49"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TPAMI.2015.2437377","article-title":"Human-machine CRFs for identifying bottlenecks in scene understanding","volume":"38","author":"Mottaghi","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","first-page":"82","article-title":"Data-driven scene understanding with adaptively retrieved exemplars","volume":"22","author":"Liu","year":"2015","journal-title":"IEEE Multidiscip."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","article-title":"Aggregating local image descriptors into compact codes","volume":"34","author":"Jegou","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5533","DOI":"10.1007\/s11042-015-2524-6","article-title":"Image classification based on improved VLAD","volume":"75","author":"Long","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cheng, C., Long, X., and Li, Y. (2019, January 22\u201324). VLAD Encoding Based on LLC for Image Classification. Proceedings of the 2019 11th International Conference on Machine Learning and Computing, Zhuhai, China.","DOI":"10.1145\/3318299.3318322"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:05Z","timestamp":1760176205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,10]]},"references-count":45,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143871"],"URL":"https:\/\/doi.org\/10.3390\/s20143871","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,10]]}}}