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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sun, Aixin Truong, Ba Quan Bhowmick, Sourav S. |
| Abstract | Despite considerable progress in recent years on Tag-based Social Image Retrieval (TagIR), state-of-the-art TagIR systems fail to provide a systematic framework for end users to ask why certain images are not in the result set of a given query and provide an explanation for such missing results. However, as humans, such why-not questions are natural when expected images are missing in the query results returned by a TagIR system. Clearly, it would be very helpful to users if they could pose follow-up why-not questions to seek clarifications on missing images in query results. In this work, we take the first step to systematically answer the why-not questions posed by end-users on TagIR systems. Our answer not only involves the reason why desired images are missing in the results but also suggestion on how the query can be altered so that the user can view these missing images in sufficient number. We present three explanation models, namely result reordering, query relaxation, and query substitution, that enable us to explain a variety of why-not questions. We present an algorithm called WINE (Why-not questIon aNswering Engine) that exploits these models to answer why-not questions efficiently. Experiments on NUS-WIDE dataset demonstrate effectiveness as well as benefits of WINE. |
| Starting Page | 917 |
| Ending Page | 926 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450324045 |
| DOI | 10.1145/2502081.2502098 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Flickr Tag-based image search Explanation models Social image Why-not questions |
| Content Type | Text |
| Resource Type | Article |
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