A new, semantically meaningful technique for querying the images from a spectral image database is proposed. The technique is based on the use of both color- and texture features. The color features are calculated from spectral images by using the Self-Organizing Map (SOM) when methods of Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are used for constructing the texture features. The importance of texture features in a querying is seen in experimental results, which are given by using a real spectral image database. Also the differences between the results gained by the use of co-occurrence matrix and LBP are introduced.
A co-occurrence matrix and Self-Organizing Map (SOM) based technique for searching images from a spectral image database is proposed. At first the SOM is trained and the Best Matching Unit (BMU) histogram is created for every spectral image of a database. Next, the texture-histogram is calculated from the co-occurrence matrices, generated using the 1st inner product images of the spectral images. BMU-histogram and the texture-histogram are combined to one feature histogram and these histograms, generated for each spectral image of a database, are saved to a histogram database. The dissimilarities between the histogram of the query image and the histograms of the database are calculated using different distance measures, more precisely Euclidean distance, dynamic partial distance and Jeffrey divergence. Finally, the images are ordered according to the histogram dissimilarity. The results using a real spectral image database are given.