Melanie A. Veltman
ABSTRACT
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.
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Index Terms
- Impediments to general purpose Content Based Image search
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Reviews
Marco Cristani
This paper shows an interesting snapshot of online visual search engines, analyzing some recent examples: Behold, Tiltomo Beta, Piximilar, and Riya Beta. With some examples, the semantic gap-the gap between the description of the class of images we want to extract and what we actually obtain from the search engine-is highlighted. In particular, the added value of performing text recognition on the images is presented. What do we get from the paper__?__ In my opinion, it introduces the main problems related to image indexing and retrieval with simple examples. It is also an invitation to learn more, by reading more technical papers. The language is easy to understand and the layout of the paper is enriched with several clarifying illustrations. Online Computing Reviews Service
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