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
Communications of the ACM - A game experience in every application
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Similarity learning via dissimilarity space in CBIR
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Visual islands: intuitive browsing of visual search results
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback
IEEE Transactions on Multimedia
Mining Multilevel Image Semantics via Hierarchical Classification
IEEE Transactions on Multimedia
Proceedings of the 1st ACM international workshop on Connected multimedia
| Hi-index | 0.00 |
Experiential image retrieval systems aim to provide the user with a natural and intuitive search experience. The goal is to empower the user to navigate large collections based on his own needs and preferences, while simultaneously providing him with an accurate sense of what the database has to offer. In this paper we integrate a new browsing mechanism called deep exploration with the proven technique of retrieval by relevance feedback. In our approach, relevance feedback focuses the search on relevant regions, while deep exploration facilitates transparent navigation to promising regions of feature space that would normally remain unreachable. Optimal feature weights are determined automatically based on the evidential support for the relevance of each single feature. To achieve efficient refinement of the search space, images are ranked and presented to the user based on their likelihood of being useful for further exploration.