C4.5: programs for machine learning
C4.5: programs for machine learning
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based query of image databases: inspirations from text retrieval
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Are two pictures better than one?
ADC '01 Proceedings of the 12th Australasian database conference
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Multiple Example Queries in Content-Based Image Retrieval
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Machine Learning
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
PicToSeek: combining color and shape invariant features for image retrieval
IEEE Transactions on Image Processing
Hi-index | 0.01 |
A common approach to content-based image retrieval is to use example images as queries; images in the collection that have low-level features similar to the query examples are returned in response to the query. In this paper, we explore the use of image regions as query examples. We compare the retrieval effectiveness of using whole images, single regions, and multiple regions as examples. We also compare two approaches for combining shape features: an equal-weight linear combination, and classification using machine learning algorithms. We show that using image regions as query examples leads to higher effectiveness than using whole images, and that an equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm.