Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
An outline of a general model for information retrieval systems
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
RELIEF: combining expressiveness and rapidity into a single system
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explicit query formulation with visual keywords
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Image retrieval based on object's orientation spatial relationship
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Symbolic photograph content-based retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Spatial layout representation for query-by-sketch content-based image retrieval
Pattern Recognition Letters
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
EMIR2: An Extended Model for Image Representation and Retrieval
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
Framework for Synthesizing Semantic-Level Indices
Multimedia Tools and Applications
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Experimental result analysis for a generative probabilistic image retrieval model
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Classification of user image descriptions
International Journal of Human-Computer Studies
A full-text framework for the image retrieval signal/semantic integration
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
IEEE Transactions on Image Processing
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
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In order to overcome the semantic gap (i.e. the gap between low-level extracted features and semantic description in state-of-the-art content-based image retrieval systems, a class of frameworks proposed within the framework of the European Fermi project, consisted of modeling the semantic content of images following a sharp process of human-assisted indexing. These approaches, based on expressive knowledge-based representation models provide satisfactory results in terms of retrieval quality but are not easily usable on large collections of images because of the necessary human intervention required for indexing. We propose in this paper to integrate the content-based and semantic-based solutions through a model featuring semantic and relational characterizations of the multimedia (image) content for automatic symbolic indexing and retrieval. Its instantiation as an image retrieval framework relies on a representation formalism handling high-level image descriptions and allowing to query with conceptual descriptors. Our approach complements content-based solutions through the mapping of low-level extracted features to semantic concepts and the manipulation of graph-based symbolic index and query structures; and extends the semantic-based solutions by considering automatically-extracted semantic and relational information. At the experimental level, we evaluate the retrieval performance of our system on queries coupling both semantic and relational characterizations through recall and precision indicators on a test collection of 2,500 color photographs and the TRECVID keyframe corpus.