Interval-valued fuzzy sets and “compensatory AND”
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
A formula for incorporating weights into scoring rules
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of visual information retrieval
Principles of visual information retrieval
Principles of visual information retrieval
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Databases: Search and Retrieval of Digital Imagery
Image Databases: Search and Retrieval of Digital Imagery
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns
Information Processing and Management: an International Journal
Learning Feature Relevance and Similarity Metrics in Image Databases
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
WebMedia '06 Proceedings of the 12th Brazilian Symposium on Multimedia and the web
Applying logistic regression to relevance feedback in image retrieval systems
Pattern Recognition
On the Combination of Ridgelets Descriptors for Symbol Recognition
Graphics Recognition. Recent Advances and New Opportunities
A genetic programming framework for content-based image retrieval
Pattern Recognition
Selection of Suitable Set of Decision Rules Using Choquet Integral
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
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We examine the effect of mathematical aggregation operators on the image retrieval performance, by empirically comparing 67 operators, applied to the problem of computing the image similarity, given a collection of individual feature similarities. While most of the existing image similarity models express the overall image similarity as an aggregation of multiple feature similarities, no study presents a comprehensive comparison of the different operators. For the comparison, we use a diverse test collection with around 2500 images in 62 semantic categories. Results show that the retrieval peformance strongly depends on the mathematical aggregation operator(s) employed within the image similarity model-the difference in the average retrieval precision between the best performing and the worst performing of the 67 operators is over 40%. Based on this observation, we propose a genetic algorithm-based relevance feedback technique--called Local Aggregation Pattern (LAP)-which adapts the image similarity model to the user by modifying the combination of aggregation operators employed within the model to aggregate multiple feature similarities into the overall image similarity. Evaluated on the 2500 images test collection, the proposed LAP technique is shown to outperform the existing relevance feedback techniques-by over 5% higher average retrieval precision. Furthermore, by modifying the combination of aggregation operators rather than the relevance of image features, the proposed LAP technique is complementary to the majority of the existing relevance feedback techniques, with which it can be naturally coupled to further improve the image retrieval performance.