Visual Location Recognition Based on Coarse-to-Fine Image Retrieval and Epipolar Geometry Constraint for Urban Environment

Feng, Guanyuan; Ma, Lin; Tan, Xiaoyang; Xue, Hao; Guan, Kai

doi:10.14257/ijsip.2016.9.11.03

Cited by 1 publication

(1 citation statement)

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“…This problem is very challenging under active research, over the past few years, particularly with the increase in the number of images and datasets that are publicly available on the Internet, which provide an opportunity for research in predicting the geographic location of images. Due to the possibility of various applications in landmark recognition [1][2][3], urban reconstruction [4], place recognition [5][6][7], visual navigation [8,9], building recognition [10,11], and robot vision [12,13], attention from the research community has been drawn to the location estimation of images [14][15][16][17][18][19][20][21] over the past decade.…”

Section: Introductionmentioning

confidence: 99%

Improving Street View Image Classification Using Pre-trained CNN Model Extracted Features

Djouadi

Kholladi²

2022

Period. Polytech. Elec. Eng. Comp. Sci.

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This paper presents a new approach for the challenging problem of image geo-localization using Convolutional Neural Networks (CNNs). This latter has become the state-of-the-art technique in computer vision and machine learning, particularly in location recognition of images taken in urban environments where the recognition accuracy is very impressive. We cast this task as a classification problem. First, we extract features from images by using pre-trained CNN model AlexNet as a feature extraction tool; where the output of the fully connected layer is considered as the feature representation. Then, the features extracted from the fully connected layer can be used for the classification process by feeding them into the Support Vector Machine (SVM) classifier. We evaluated the proposed approach on a data set of Google Street View images (GSV); the experimental results show that our approach can improve the classification by achieving a good accuracy rate which is 94.19%.

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Section: Introductionmentioning

confidence: 99%

Improving Street View Image Classification Using Pre-trained CNN Model Extracted Features

Djouadi

Kholladi²

2022

Period. Polytech. Elec. Eng. Comp. Sci.

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show abstract

Visual Location Recognition Based on Coarse-to-Fine Image Retrieval and Epipolar Geometry Constraint for Urban Environment

Abstract: Visual based location recognition of a mobile device is an important problem in

Cited by 1 publication

References 13 publications

Improving Street View Image Classification Using Pre-trained CNN Model Extracted Features

Improving Street View Image Classification Using Pre-trained CNN Model Extracted Features

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