Image Retrieval Based on Regions of Interest
IEEE Transactions on Knowledge and Data Engineering
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Tag localization with spatial correlations and joint group sparsity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
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Query suggestion is an effective solution to help users deliver their search intent. While many query suggestion approaches have been proposed for test-based image retrieval with query-by-keywords, query suggestion for content-based image retrieval (CBIR) with query-by-example (QBE) has been seldom studied. QBE usually suffers from the "intention gap" problem, especially when the user fails to get an appropriate query image to express his search intention precisely. In this paper, we propose a novel query suggestion scheme named Visual Query Attributes Suggestion (VQAS) for image search with QBE. Given a query image, informative attributes are suggested to the user as complements to the query. These attributes reflect the visual properties and key components of the query. By selecting some suggested attributes, the user can provide more precise search intent which is not captured by the query image. The evaluation results on two real-world image datasets show the effectiveness of VQAS in terms of retrieval performance and the quality of query suggestions.