Human-centered object-based image retrieval

Authors:
Egon L. Van Den Broek;Eva M. Van Rikxoort;Theo E. Schouten
Affiliations:
Department of Artificial Intelligence, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands;Institute for Computing and Information Science, Radboud University Nijmegen, Nijmegen, The Netherlands
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

A new object-based image retrieval (OBIR) scheme is introduced. The images are analyzed using the recently developed, human-based 11 colors quantization scheme and the color correlogram. Their output served as input for the image segmentation algorithm: agglomerative merging, which is extended to color images. From the resulting coarse segments, boundaries are extracted by pixelwise classification, which are smoothed by erosion and dilation operators. The resulting features of the extracted shapes, completed the data for a -vector. Combined with the intersection distance measure, this vector is used for OBIR, as are its components. Although shape matching by itself provides good results, the complete vector outperforms its components, with up to 80% precision. Hence, a unique, excellently performing, fast, on human perception based, OBIR scheme is achieved.