Issues concerning dimensionality and similarity search
Effectiveness and efficiency are two important concerns in using multimedia descriptors to search and access database items. Both are affected by the dimensionality of the descriptors. While higher dimensionality generally increases effectiveness, it drastically reduces efficiency of storage and searching. With regard to effectiveness, relevance feedback is known to be a useful tool to squeeze information from a descriptor. However, not much has been done toward enabling relevance feedback computation using high-dimensional descriptors over a large multimedia dataset. In this context, we have developed new methods that enable us to a) reduce the dimensionality of Gabor texture descriptors without losing on effectiveness, and b) perform fast nearest neighbor search based on the information available during each iteration of a relevance feedback step. Experimental results are presented on real datasets.