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Combining image descriptors to effectively retrieve events from visual lifelogs (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Doherty, Aiden R. Conaire, Ciarán Ó Blighe, Michael Smeaton, Alan F. Noel, E. |
| Description | The SenseCam is a wearable camera that passively captures approximately 3,000 images per day, which equates to almost one million images per year. It is used to create a personal visual recording of the wearer’s life and generates information which can be helpful as a human memory aid. For such a large amount of visual information to be of any use, it is accepted that it should be structured into“events”, of which there are about 8,000 in a wearer’s average year. In automatically segmenting SenseCam images into events, it will then be useful for users to locate other events similar to a given event e.g. “what other times was I walking in the park?”, “show me other events when I was in a restaurant”. On two datasets of 240k and 1.8M images containing topics with a variety of information needs, we evaluate the fusion of MPEG-7, SIFT, and SURF content-based retrieval techniques to address the event search issue. We have found that our proposed fusion approach of MPEG-7 and SURF offers an improvement on using either of those sources or SIFT individually, and we have also shown how a lifelog event is modeled has a large effect on the retrieval performance. |
| File Format | |
| Language | English |
| Publisher | ACM |
| Publisher Date | 2008-01-01 |
| Publisher Institution | In MIR ’08: Proceeding of the 1st ACM international conference on Multimedia information retrieval |
| Access Restriction | Open |
| Subject Keyword | Event Search Issue Large Amount Personal Visual Recording Information Need Visual Lifelogs Human Memory Aid Surf Content-based Retrieval Technique Visual Information Retrieval Performance Sensecam Image Wearer Average Year Generates Information Wearer Life Image Descriptor Large Effect Fusion Approach Lifelog Event Wearable Camera |
| Content Type | Text |
| Resource Type | Article |
