The query by image content (QBIC) system
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms
IDMS '97 Proceedings of the 4th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
An online system for gathering image similarity judgements
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
IEEE Transactions on Information Theory
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Challenges faced by prevailing text metadata paradigms for online image search have inspired overwhelming research in Content Based Image Retrieval (CBIR). A multitude of approaches have been introduced within the literature, yet relatively few image search engines have been made publicly available on the web. Aside from challenges facing the user, such as describing a visual query using keywords, or finding an appropriate example image to initiate a visual search, all systems must inevitably grapple with the sensory and semantic gaps [Smeulders et al. 2000], which essentially represent a loss of information in the abstraction process. In this work, we challenge commonly suggested approaches to improving CBIR and illustrate drawbacks of relying on textual data, as well as visual data, in general CBIR search. We provide cogent examples using online visual search engines Behold™, Tiltomo Beta, Pixilimar, and Riya™ Beta. These examples demonstrate the effect of semantic ambiguities in natural language, which extend to search terms and text tags.