Texture classification using multiresolution Markov random field models
Pattern Recognition Letters
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Mining Patterns of Change in Remote Sensing Image Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of weak textures. The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known. In this work, we have proposed the use of a new feature ‘texton histogram' to capture the weak-textured nature of remote sensing images. Combined with an automatic classifier, our texton histograms are robust to variations in scale, orientation and illumination conditions as illustrated experimentally. The classification accuracy is further improved using additional image driven features obtained by the application of a feature selection procedure.