International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Image region entropy: a measure of "visualness" of web images associated with one concept
Proceedings of the 13th annual ACM international conference on Multimedia
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive salient block-based image retrieval in multi-feature space
Image Communication
Combining low-level features for semantic extraction in image retrieval
EURASIP Journal on Advances in Signal Processing
A knowledge synthesizing approach for classification of visual information
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
A bayesian network approach to multi-feature based image retrieval
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Object recognition using summed features classifier
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Building detection with loosely-coupled hybrid feature descriptors
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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This paper proposes a novel approach for the construction and use of multi-feature spaces in image classification. The proposed technique combines low-level descriptors and defines suitable metrics. It aims at representing and measuring similarity between semantically meaningful objects within the defined multi-feature space. The approach finds the best linear combination of predefined visual descriptor metrics using a Multi-Objective Optimization technique. The obtained metric is then used to fuse multiple non-linear descriptors is be achieved and applied in image classification.