CASIS : a system for Concept-Aware Social Image Search
Tag-based social image search enables users to formulate queries using keywords. However, as queries are usually very short and users have very different interpretations of a particular tag in annotating and searching images, the returned images to a tag query usually contain a collection of images...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/97051 http://hdl.handle.net/10220/11708 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Tag-based social image search enables users to formulate queries using keywords. However, as queries are usually very short and users have very different interpretations of a particular tag in annotating and searching images, the returned images to a tag query usually contain a collection of images related to multiple concepts. We demonstrate Casis, a system for concept-aware social image search. Casis detects tag concepts based on the collective knowledge embedded in social tagging from the initial results to a query. A tag concept is a set of tags highly associated with each other and collectively conveys a semantic meaning. Images to a query are then organized by tag concepts. Casis provides intuitive and interactive browsing of search results through a tag concept graph, which visualizes the tags defining each tag concept and their relationships within and across concepts. Supporting multiple retrieval methods and multiple concept detection algorithms, Casis offers superior social image search experiences by choosing the most suitable retrieval methods and concept-aware image organizations. |
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