Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Systems - Special issue on content-based retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient Matching and Indexing of Graph Models in Content-Based Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Fusion Via a Linear Combination of Scores
Information Retrieval
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
A Formal Definition of Binary Topological Relationships
FOFO '89 Proceedings of the 3rd International Conference on Foundations of Data Organization and Algorithms
Reasoning About Spatial Relationships in Picture Retrieval Systems
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Comparison of Conceptual Graphs
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A signal/semantic framework for image retrieval
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
Performance prediction of data fusion for information retrieval
Information Processing and Management: an International Journal
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting spatial context constraints for automatic image region annotation
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
The segmented and annotated IAPR TC-12 benchmark
Computer Vision and Image Understanding
Markov random fields and spatial information to improve automatic image annotation
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Overview of the ImageCLEFphoto 2008 photographic retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Data fusion and label weighting for image retrieval based on spatio-conceptual information
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Weighted walkthroughs between extended entities for retrieval by spatial arrangement
IEEE Transactions on Multimedia
Trends in semantic and digital media technologies
Multimedia Tools and Applications
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In this paper we proposed the use of spatial relations as a way of improving annotation-based image retrieval. We analyzed different types of spatial relations and selected the most adequate ones for image retrieval. We developed an image comparison and retrieval method based on conceptual graphs, which incorporates spatial relations. Additionally, we proposed an alternative term-weighting scheme and explored the use of more than one sample image for retrieval using several late fusion techniques. Our methods were evaluated with a rich and complex image dataset, based on the 39 topics developed for the ImageCLEF 2008 photo retrieval task. Results show that: (i) incorporating spatial relations produces a significant increase in performance, (ii) the label weighting scheme we proposed obtains better results than other traditional schemes, and (iii) the combination of several sample images using late fusion produces an additional improvement in retrieval according to several metrics.