Kernel indexing for relevance feedback image retrieval

Jing Peng, Douglas R. Heisterkamp

Research output: Contribution to conferencePaperpeer-review

12 Scopus citations


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 non-linear 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.

Original languageEnglish
Number of pages4
StatePublished - 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: 14 Sep 200317 Sep 2003


OtherProceedings: 2003 International Conference on Image Processing, ICIP-2003


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