Automated feature selection through relevance feedback

The VisiMine project aims to provide infrastructure that would enable the analysis of large databases containing satellite images. Our work addresses two issues. One is the extraction of information that enables reduction of the data from multi-spectral images into a number of features. Second is the organization and selection of the features that would allow flexible and scalable discovery of the knowledge from the databases of remotely sensed images. The VisiMine architecture distinguishes between three types of feature vectors: pixel, region and tile. One of the challenges in information retrieval is the proper choice of the set of features that are the best suited for a data mining task. The VisiMine system enables extraction of a large number of features that describe textural and spectral properties of satellite information, in addition to the analysis of image information, the system can perform data fusion of image properties with auxiliary data such as DEM. Tilton et al. (2002) presented the results of the information retrieval experiments with the Hierarchical Segmentation (HSEG) algorithm that produces a hierarchical set of image segmentations. The results presented showed that the use of HSEG features improves the precision and recall of similarity searches. However, for different types of land cover, different combinations of HSEG segmentation levels and textural features provided the best results. Image analysis applications often require different levels of image segmentation detail as well as the use of different mixes of spectral, textural and shape features combined together with auxiliary information. Furthermore, a particular application may require different features and different levels of image segmentation detail depending on how the image objects are being analyzed. Thus, an automatic selection of feature sets would be very useful for satellite image analysis. In this paper, we present algorithms that allow for automatic selection of features for region and tile similarity searches. The relevance feedback technique allows for selective choices to be made in the region(s) of interest for which a good subset of features may be found in real time. The preliminary results of the experiments with LANDSAT data show improvements in both prec- ision and recall over previously used methods.