A fuzzy extended k-nearest neighbors rule
Fuzzy Sets and Systems
International Journal of Computer Vision
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Human-based spatial relationship generalization through neural/fuzzy approaches
Fuzzy Sets and Systems
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge-based metadata extraction from PostScript files
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic metadata generation & evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
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Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-based linguistic indexing of pictures with 2--d MHMMs
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Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
R-Histogram: quantitative representation of spatial relations for similarity-based image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
On the edited fuzzy K-nearest neighbor rule
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Linguistic description of relative positions in images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Finding a catalog: generating analytical catalog records from well-structured digital texts
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Combining global and local information for knowledge-assisted image analysis and classification
EURASIP Journal on Advances in Signal Processing
Speaking with spatial relations
International Journal of Intelligent Systems Technologies and Applications
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
A graph-based fuzzy linguistic metadata schema for describing spatial relationships
Proceedings of the 2011 Visual Information Communication - International Symposium
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Automatic generation of semantic metadata describing spatial relations is highly desirable for image digital libraries Relative spatial relations between objects in an image convey important information about the image. Because the perception of spatial relations is subjective, we propose a novel framework for automatic metadata generation based on fuzzy k-NN classification that generates fuzzy semantic metadata describing spatial relations between objects in an image. For each pair of objects of interest, the corresponding R-Histogram is computed and used as input for a set of fuzzy k--NN classifiers. The R-Histogram is a quantitative representation of spatial relations between two objects The outputs of the classifiers are soft class labels for each of the following eight spatial relations: 1) LEFT OF, 2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE, 8) OUTSIDE Because the classifier-training stage involves annotating the training images manually, it is desirable to use as few training images as possible. To address this issue, we applied existing prototype selection techniques and also devised two new extensions. We evaluated the performance of different fuzzy k-NN algorithms and prototype selection algorithms empirically on both synthetic and real images. Preliminary experimental results show that our system is able to obtain good annotation accuracy (92%--98% on synthetic images and 82%--93% on real images) using only a small training set (4--5 images).