The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
WhittleSearch: Image search with relative attribute feedback
Abstract
We propose a novel mode of feedback for image search, where a user describes which properties of exemplar images should be adjusted in order to more closely match his/her mental model of the image(s) sought. For example, perusing image results for a query “black shoes”, the user might state, “Show me shoe images like these, but sportier.” Offline, our approach first learns a set of ranking functions, each of which predicts the relative strength of a nameable attribute in an image (`sportiness', `furriness', etc.). At query time, the system presents an initial set of reference images, and the user selects among them to provide relative attribute feedback. Using the resulting constraints in the multi-dimensional attribute space, our method updates its relevance function and re-ranks the pool of images. This procedure iterates using the accumulated constraints until the top ranked images are acceptably close to the user's envisioned target. In this way, our approach allows a user to efficiently “whittle away” irrelevant portions of the visual feature space, using semantic language to precisely communicate her preferences to the system. We demonstrate the technique for refining image search for people, products, and scenes, and show it outperforms traditional binary relevance feedback in terms of search speed and accuracy.
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Cites
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
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Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Optimizing search engines using clickthrough data
Joachims, T. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775067 · Cited by 3,873
Query by image and video content: the QBIC system
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Training linear SVMs in linear time
Joachims, T. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150429 · Cited by 1,920
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917
Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., & Harmeling, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206594 · Cited by 1,849
Relevance feedback: a power tool for interactive content-based image retrieval
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Query by Image and Video Content: The QBIC System
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., et al. (2002) · Readings in Multimedia Computing and Networking · doi:10.1016/b978-155860651-7/50108-x · Cited by 1,559
Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., & Harmeling, S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206594 · Cited by 1,528
Cited by
Attribute-Based Classification for Zero-Shot Visual Object Categorization
Lampert, C. H., Nickisch, H., & Harmeling, S. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.140 · Cited by 1,627
Deep Learning the City: Quantifying Urban Perception at a Global Scale
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_12 · Cited by 513
Self-citationFine-Grained Visual Comparisons with Local Learning
Yu, A., & Grauman, K. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.32 · Cited by 454
Self-citationLearning visual similarity for product design with convolutional neural networks
Bell, S., & Bala, K. (2015) · ACM Transactions on Graphics · doi:10.1145/2766959 · Cited by 408
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
Wu, H., Gao, Y., Guo, X., Al-Halah, Z., Rennie, S., Grauman, K., & Feris, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01115 · Cited by 264
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Metadata
- Authors
- Kovashka, A., Parikh, D., & Grauman, K.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2973-2980
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2033365921
- Cited by
- 296
- References
- 29
- FWCI
- 24.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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