Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Generic image classification using visual knowledge on the web
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Context modeling and discovery using vector space bases
Proceedings of the 14th ACM international conference on Information and knowledge management
Using Language to Drive the Perceptual Grouping of Local Image Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified image retrieval framework on local visual and semantic concept-based feature spaces
Journal of Visual Communication and Image Representation
Towards predicting relevance using a quantum-like framework
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Integrating text retrieval and image retrieval in XML document searching
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
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Multimedia information retrieval suffers from the semantic gap, a difference between human perception and machine representation of images. In order to reduce the gap, a quantum theory inspired theoretical framework for integration of text and visual features has been proposed. This article is a followup work on this model. Previously, two relatively straightforward statistical approaches for making associations between dimensions of both feature spaces were employed, but with unsatisfactory results. In this paper, we propose to alleviate the problem regarding unannotated images by projecting them onto subspaces representing visual context and by incorporating a quantum-like measurement. The proposed principled approach extends the traditional vector space model (VSM) and seamlessly integrates with the tensor-based framework. Here, we experimentally test the novel association methods in a small-scale experiment.