Pattern Recognition Letters - Special issue on genetic algorithms
ACM Computing Surveys (CSUR)
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction for Locally Weighted Learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Linear regression for dimensionality reduction and classification of multi dimensional data
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
| Hi-index | 0.10 |
In case of spatial multi spectral images, such as remotely sensed earth cover, there could be many classes in one entire frame covering a large spatial stretch, because of which meaningful dimensionality reduction cannot perhaps be realizable without trading off with the quality of classification. However most often one would encounter in such images, presence of only a few classes in a small neighborhood, which would enable to devise a very effective dimensionality reduction around that small neighborhood identified as a block. Based on this theme a new method for dimensionality reduction is proposed in this paper.The method proposed divides the image into uniform non-overlapping windows/blocks. The few features that are essential in discriminating classes in a window are identified. Clustering is performed independently on each of the blocks with the reduced set of features. These clusters in the blocks are later merged to obtain an overall classification of the entire image. The efficacy of the method is corroborated experimentally.