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Multimodal Analysis of Image Search Intent : Intent Recognition in Image Search from User Behavior and Visual Content
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2018 |
| Abstract | Users search for multimedia content with different underlying motivations or intentions. Study of user search intentions is an emerging topic in information retrieval since understanding why a user is searching for a content is crucial for satisfying the user’s need. In this paper, we aimed at automatically recognizing a user’s intent for image search in the early stage of a search session. We designed seven different search scenarios under the intent conditions of finding items, re-finding items and entertainment. We collected facial expressions, physiological responses, eye gaze and implicit user interactions from 51 participants who performed seven different search tasks on a custom-built image retrieval platform. We analyzed the users’ spontaneous and explicit reactions under different intent conditions. Finally, we trained machine learning models to predict users’ search intentions from the visual content of the visited images, the user interactions and the spontaneous responses. After fusing the visual and user interaction features, our system achieved the F-1 score of 0.722 for classifying three classes in [...] SOLEYMANI, Mohammad, RIEGLER, Michael, HALVORSEN, Pål. Multimodal Analysis of Image Search Intent: Intent Recognition in Image Search from User Behavior and Visual Content. In: ICMR '17 Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. ACM Press, 2017. DOI : 10.1145/3078971.3078995 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://archive-ouverte.unige.ch/files/downloads/0/0/1/0/2/2/1/6/unige_102216_attachment01.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
