IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Nearest-Prototype Relevance Feedback for Content Based Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Similarity learning via dissimilarity space in CBIR
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Dissimilarity representation of images for relevance feedback in content-based image retrieval
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Learning user queries in multimodal dissimilarity spaces
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
| Hi-index | 0.00 |
In this paper we propose a novel approach to combine information form multiple high-dimensional feature spaces, which allows reducing the computational time required for image retrieval tasks. Each image is represented in a "(dis)similarity space", where each component is computed in one of the low-level feature spaces as the (dis)similarity of the image from one reference image. This new representation allows the distances between images belonging to the same class being smaller than in the original feature spaces. In addition, it allows computing similarities between images by taking into account multiple characteristics of the images, and thus obtaining more accurate retrieval results. Reported results show that the proposed technique allows attaining good performances not only in terms of precision and recall, but also in terms of the execution time, if compared to techniques that combine retrieval results from different feature spaces.