A multimedia data base browsing system
Proceedings of the 1st international workshop on Computer vision meets databases
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Hierarchical fuzzy feature similarity combination for presentation slide retrieval
EURASIP Journal on Advances in Signal Processing
Content based image retrieval using unclean positive examples
IEEE Transactions on Image Processing
Series feature aggregation for content-based image retrieval
Computers and Electrical Engineering
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
Spectral Regression dimension reduction for multiple features facial image retrieval
International Journal of Biometrics
Proceedings of the 14th International Conference on Computer Systems and Technologies
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The large volumes of artistic visual data available to museums, art galleries, and online collections motivate the need for effective means to retrieve :relevant information from such repositories. The paper proposes a decision making framework for content-based retrieval of art images based on a combination of low-level features. Traditionally, the similarity between two images has been calculated as a weighted distance between two feature vectors. This approach, however, may not be mathematically and computationally appropriate, and does not provide enough flexibility in modeling user queries. The paper proposes a framework that generalizes a wide set of previous approaches to similarity calculation, including the weighted distance approach. Image similarities are obtained through a decision making process based on low-level feature distances using fuzzy theory. The analysis and results indicate that the presented aggregation technique provides an effective, general, and flexible tool for similarity calculation based on the combination of individual descriptors and features.