Vector quantization and signal compression
Vector quantization and signal compression
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Keyblock: an approach for content-based image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Integrated Region-Based Image Retrieval
Integrated Region-Based Image Retrieval
Concept-Based Visual Information Management with Large Lexical Corpus
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Good Features to Track
Content-Based Hierarchical Classification of Vacation Images
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Semantic image segmentation with a multidimensional hidden markov model
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Visual context descriptor (VCD) is a new image representation scheme for visual content classification. It consists of a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region thereof. VCD utilizes the predetermined quality dimensions, such as types of features and quantization level, along with predetermined semantic model templates. The observed visual cues and the contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector, say a color histogram or a Gabor texture, into a discrete event, e. g., terms in the text domain.