Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards ontology-based cognitive vision
Machine Vision and Applications
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Ontology based complex object recognition
Image and Vision Computing
Semantic representation of multimedia content
Knowledge-driven multimedia information extraction and ontology evolution
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This paper presents a new approach for building semantic image indexing and retrieval systems. Our approach is composed of four phases : (1) knowledge acquisition, (2) weakly-supervised learning, (3) indexing and (4) retrieval. Phase 1 is driven by a visual concept ontology which helps the expert to define low-level features useful to characterize object classes. Phase 2 uses acquired knowledge and image samples to learn the mapping between image data and visual concepts. Image indexing phase (phase 3) is fully automatic and produces semantic annotations of the images to index. The symbolic nature of querying enables user-friendly and fast retrieval (phase 4). We have applied our approach to the domain of transport vehicles (i.e. motorbikes, aircrafts, cars).