VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
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ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
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PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
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NOLASC'09 Proceedings of the 8th WSEAS international conference on Non-linear analysis, non-linear systems and chaos
Use of image regions in context-adaptive image classification
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
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The content-based image retrieval (CBIR) system PicSOM uses a variety of low-level visual features for indexing an image database. In this paper we describe the implementation of segmentation into the PicSOM framework. That is, we have modified the system to use image segments as a supplement to entire images in order to improve the retrieval accuracy. In a series of experiments, we compare this new method to the baseline PicSOM system. The results confirm that using both segments and entire images together always increases the precision of retrieval.