Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Evaluation of Feature Descriptors for Scene Classification
The current article discusses the performance of local and global descriptors, as well as convolutional neural networks (CNNs), in tasks involving image recognition in interior spaces. The purpose of the test is to identify several realistic situations that closely resemble the typical working conditions for mobile robots. A robot interacting with its environment may be able to see portions of scenes in which objects are seen from various angles or changes in the lighting in various settings. The purpose is to investigate how well the different descriptors perform in identifying situations that meet the above criteria. In order to evaluate the effectiveness of visual descriptors and convolutional neural networks in the classification of images taken from the perspective of mobile robots in indoor environments, a proprietary database was implemented and subjected to several controlled transformations. These modifications made it possible to analyze the performance of Bag-of-Visual-Words (BoVW), Fisher Vectors (Fisher), Vector of Locally Aggregated Descriptors (VLAD), Global Image Descriptors (GIST), and CNN descriptors in visual categorization tasks according to the situational perception of mobile robots.The findings highlight the advantages of descriptors for the various test scenarios and highlight the need for hybrid models that employ both descriptors and CNNs for scene identification tasks in interior areas where mobile robots operate.
Evaluation of Feature Descriptors for Scene Classification
The current article discusses the performance of local and global descriptors, as well as convolutional neural networks (CNNs), in tasks involving image recognition in interior spaces. The purpose of the test is to identify several realistic situations that closely resemble the typical working conditions for mobile robots. A robot interacting with its environment may be able to see portions of scenes in which objects are seen from various angles or changes in the lighting in various settings. The purpose is to investigate how well the different descriptors perform in identifying situations that meet the above criteria. In order to evaluate the effectiveness of visual descriptors and convolutional neural networks in the classification of images taken from the perspective of mobile robots in indoor environments, a proprietary database was implemented and subjected to several controlled transformations. These modifications made it possible to analyze the performance of Bag-of-Visual-Words (BoVW), Fisher Vectors (Fisher), Vector of Locally Aggregated Descriptors (VLAD), Global Image Descriptors (GIST), and CNN descriptors in visual categorization tasks according to the situational perception of mobile robots.The findings highlight the advantages of descriptors for the various test scenarios and highlight the need for hybrid models that employ both descriptors and CNNs for scene identification tasks in interior areas where mobile robots operate.
Evaluation of Feature Descriptors for Scene Classification
Lect. Notes in Networks, Syst.
Castillo Ossa, Luis Fernando (Herausgeber:in) / Isaza, Gustavo (Herausgeber:in) / Cardona, Óscar (Herausgeber:in) / Castrillón, Omar Danilo (Herausgeber:in) / Corchado Rodriguez, Juan Manuel (Herausgeber:in) / De la Prieta Pintado, Fernando (Herausgeber:in) / Hernando Ríos González, Luis (Autor:in) / López Flórez, Sebastián (Autor:in) / González-Briones, Alfonso (Autor:in) / de la Prieta, Fernando (Autor:in)
Sustainable Smart Cities and Territories International Conference ; 2023 ; Manizales, Colombia
02.09.2023
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Unsupervised feature learning for aerial scene classification
| Online Contents | 2014
Unsupervised Feature Learning for Aerial Scene Classification
| Online Contents | 2014
Unsupervised Feature Learning for Aerial Scene Classification
| Online Contents | 2014
Saliency-Guided Unsupervised Feature Learning for Scene Classification
| Online Contents | 2015
Deep Feature Fusion for VHR Remote Sensing Scene Classification
| Online Contents | 2017