Techniques of Indoor-Outdoor Scene Classification using the VGG-16 CNN Model
Kajal Gupta1, RK Sharma2
1Kajal Gupta*, M. Tech degree, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Agra College, Agra (U.P.), India.
2R K Sharma, Associate Professor, Faculty of Engineering and Technology, Agra College, Agra (U.P.), India.
Manuscript received on July 19, 2021. | Revised Manuscript received on July 26, 2021. | Manuscript published on July 30, 2021. | PP: 242-247 | Volume-10 Issue-2, July 2021. | Retrieval Number: 100.1/ijrte.B62970710221| DOI: 10.35940/ijrte.B6297.0710221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the world of today, computers have begun to rule the people as the machines carry out practically every work that people can accomplish. Scene classification is one such concept that becomes increasingly important when robots replicate the actions of a human being Scene categorization may be done on interior or exterior scenes using various extraction techniques, as well as categorization of indoor and outdoor scenes in these two categories is more difficult. The methodology for the indoor/outdoor classification scene has the drawback of inadequate accuracy. This research aims to enhance the accuracy by using the Convolution Neural Network Model in VGG-16. This paper proposes a new approach to VGG-16 to classify images into their classes. The algorithm results are tested using SUN397- indoor-outdoor dataset & the tentative data reveal that the methodology proposed is superior to the existing technology for the scene classification of indoor-outdoor (I/U).
Keywords: Scene Classification, Indoor-Outdoor Classification, Deep Learning, Neural Network Model VGG 16, CCN, Data Augmentation, Imagedatagenerator, Optimizers.