Kernel indexing for relevance feedback image retrieval

Relevance feedback is an attractive approach to developing flexible metrics for content-based retrieval in image and video databases. Large image databases require an index structure in order to reduce nearest neighbor computation. However, flexible metrics can alter an input space in a highly nonlinear fashion, thereby rendering the index structure useless. Few systems have been developed that address the apparent flexible metric/indexing dilemma. This paper proposes kernel indexing to try to address this dilemma. The key observation is that kernel metrics may be nonlinear and highly dynamic in the input space but remain Euclidean in induced feature space. It is this linear invariance in feature space that enables us to learn arbitrary relevance functions without changing the index in feature space. As a result, kernel indexing supports efficient relevance feedback retrieval in large image databases. Experimental results using a large set of image data are very promising.