Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Contour Line and Geographic Feature Extraction from USGS Color Topographical Paper Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text/Graphics Separation in Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guest Editorial: Similarity Matching in Computer Vision and Multimedia
Computer Vision and Image Understanding
Automatic extraction of road intersection position, connectivity, and orientations from raster maps
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Data mining of maps and their automatic region-time-theme classification
SIGSPATIAL Special
A Method for Automatically Extracting Road Layers from Raster Maps
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Raster maps are widely available and contain useful geographic features such as labels and road lines. To extract the geographic features, most research work relies on a manual step to first extract the foreground pixels from the maps using the distinctive colors or grayscale intensities of the pixels. This strategy requires user interaction for each map to select a set of thresholds. In this paper, we present a map classification technique that uses an image comparison feature called the luminance-boundary histogram and a nearest-neighbor classifier to identify raster maps with similar grayscale intensity usage. We can then apply previously learned thresholds to separate the foreground pixels from the raster maps that are classified in the same group instead of manually examining each map. We show that the luminance-boundary histogram achieves 95% accuracy in our map classification experiment compared to 13.33%, 86.67%, and 88.33% using three traditional image comparison features. The accurate map classification results make it possible to extract geographic features from previously unseen raster maps.