Scene Recognition using Significant Feature Detection Technique
Priya Singla1, Rajesh Mehra2
1Er. PriyaSingla, Pursuing M.E from National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, India.
2Dr. Rajesh Mehrais, Head of Curriculum Development Center, National Institute of Technical Teacher Training & Research, Chandigarh, India
Manuscript received on December 16, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 1705-1711 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8653019320/2020©BEIESP | DOI: 10.35940/ijitee.C8653.019320
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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: Scene classification is basic problem in robotics and computer vision application. In Scene classification focused on complete view or event that contains both low and high level features. The main purpose of scene classification is to diminish the semantic gap in between social life & computer system. The main issue in scene classification is recognizing tall buildings, mountain, open country and inside city. We applied combination algorithms of feature extraction on trained datasets. Our proposed algorithm is hybrid combination of SIFT+ HOG named as HFCNN. As compare with the existing CNN architecture, HFCNN perform betters with high accuracy rate. Accuracy rate for proposed architecture is more than 96% as calculated with better time consumption and cost effective.
Keywords: Scene classification, Multimillion images, Local & global feature, Image Net and Convolutional Neural Network.
Scope of the Article: classification