Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity in multimedia information retrieval research
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A Probabilistic Model for User Relevance Feedback on Image Retrieval
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Late fusion of heterogeneous methods for multimedia image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Query refinement suggestion in multimodal image retrieval with relevance feedback
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
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A fundamental problem in image retrieval is how to improve the text-based retrieval systems, which is known as "bridging the semantic gap". The reliance on visual similarity for judging semantic similarity may be problematic due to the semantic gap between low-level content and higher-level concepts. One way to overcome this problem and increase thus retrieval performance is to consider user feedback in an interactive scenario. In our approach, a user starts a query and is then presented with a set of (hopefully) relevant images; selecting from these images those which are more relevant to her. Then the system refines its results after each iteration, using late fusion methods, and allowing the user to dynamically tune the amount of textual and visual information that will be used to retrieve similar images. We describe how does our approach fit in a real-world setting, discussing also an evaluation of results.