Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical building recognition
Image and Vision Computing
Scalable landmark recognition using EXTENT
Multimedia Tools and Applications
A Survey on Mobile Landmark Recognition for Information Retrieval
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Context-aware Discriminative Vocabulary Tree Learning for mobile landmark recognition
Digital Signal Processing
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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.