TY - GEN
T1 - A multi-scale learning approach for landmark recognition using mobile devices
AU - Chen, Tao
AU - Li, Zhen
AU - Yap, Kim Hui
AU - Wu, Kui
AU - Chau, Lap Pui
PY - 2009/12
Y1 - 2009/12
N2 - The growing usage of mobile camera phones has led to proliferation of many mobile applications. Landmark recognition is one of the mobile applications that are gaining more attention in recent years. The main idea of the application is that a user will use a camera phone to capture the image of a landmark or building and then the system will analyze, identify, and inform the user the name of the captured landmark together with its related information. A new mobile landmark recognition method is proposed in this paper: first, a set of multi-scale patches are extracted from the landmark images. Discriminative patches of the images are then selected based on a Gaussian mixture model (GMM). A combination of color, texture and scale-invariant feature transform (SIFT) descriptors are then extracted from the selected patches. They are used to train support vector machine (SVM) classifiers for each category of landmark. Experimental results using a database of 4000 landmark images illustrate the effectiveness of the proposed method.
AB - The growing usage of mobile camera phones has led to proliferation of many mobile applications. Landmark recognition is one of the mobile applications that are gaining more attention in recent years. The main idea of the application is that a user will use a camera phone to capture the image of a landmark or building and then the system will analyze, identify, and inform the user the name of the captured landmark together with its related information. A new mobile landmark recognition method is proposed in this paper: first, a set of multi-scale patches are extracted from the landmark images. Discriminative patches of the images are then selected based on a Gaussian mixture model (GMM). A combination of color, texture and scale-invariant feature transform (SIFT) descriptors are then extracted from the selected patches. They are used to train support vector machine (SVM) classifiers for each category of landmark. Experimental results using a database of 4000 landmark images illustrate the effectiveness of the proposed method.
KW - Gaussian mixture model
KW - Mobile landmark recognition
KW - Multi-scale patches
KW - Support vector machine
UR - https://www.scopus.com/pages/publications/77949602878
U2 - 10.1109/ICICS.2009.5397713
DO - 10.1109/ICICS.2009.5397713
M3 - Conference article published in proceeding or book
AN - SCOPUS:77949602878
SN - 9781424446575
T3 - ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing
BT - ICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing
T2 - 7th International Conference on Information, Communications and Signal Processing, ICICS 2009
Y2 - 8 December 2009 through 10 December 2009
ER -