Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computer and Robot Vision
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Training Templates for Scene Classification using a Few Examples
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
FOCUS: Searching for Multi-colored Objects in a Diverse Image Database
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Context and configuration-based scene classification
Context and configuration-based scene classification
Indoor versus outdoor scene classification using probabilistic neural network
EURASIP Journal on Applied Signal Processing
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We develop a framework for combining configurational and statistical approaches in image retrieval. While configurations have semantic description power, the explicit representation of an image by a set of configurations lacks the vector space structure from which the statistical feature-based representations have benefitted. That makes concept learning and prediction harder. Our framework treats configurations analogously to words occurring in a document. It combines a configuration-based approach with statistical approaches to take advantage of both the semantic description power of the former, and the simple vector-space structure of the latter.