SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGIR Forum
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Bag-of-visual-words expansion using visual relatedness for video indexing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Dynamic presentation adaptation based on user intent classification
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
MMWEB '11 Proceedings of the 2011 Workshop on Multimedia on the Web
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When searching for visual content in large image repositories users face a serious problem. Due to the large amount of resources to tap, they cannot find the content, which fits their information need best. The information need is typically based on the user's intention or the goal s/he wants to achieve and is expressed by the user's search behavior. For instance, if someone wants to buy a bicycle, the information need - find pictures of bicycles - is based on the intention to buy a bicycle. In this paper we consider users with a more specific and less specific, hence vague intent. We apply retrieval adaptation in terms of the degree of intentionality using hard and soft assignment techniques of the well known Bag of Visual Word(BoVW) retrieval method. Our evaluations on two common data sets reveal interesting results, which pose the potential of our approach.