Efficient use of local edge histogram descriptor
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improved query difficulty prediction for the web
Proceedings of the 17th ACM conference on Information and knowledge management
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Ranking model adaptation for domain-specific search
Proceedings of the 18th ACM conference on Information and knowledge management
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Dynamic captioning: video accessibility enhancement for hearing impairment
Proceedings of the international conference on Multimedia
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
The role of attractiveness in web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
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Query difficulty estimation predicts the performance of the search result of the given query. It is a powerful tool for multimedia retrieval and receives increasing attention. It can guide the pseudo relevance feedback to rerank the image search results and re-write the query by suggesting ''easy'' alternatives to obtain better search results. Many techniques to estimate the query difficulty have been proposed in the textual information retrieval, but directly employing them for image search will result in poor performance. That is because image query is more complex with spatial or structural information, and the well-known semantic gap induces extra burdens for accurate estimations. In this paper, we propose a query difficulty estimation approach by analyzing the top ranked images obtained by ad hoc retrieval models. Specifically, we seamlessly integrate the language model based clarity score, the spatial consistency of local descriptors and the appearance consistency of global features. Experimental results demonstrate that the query difficulty estimated by the proposed algorithm correlates well with the actual retrieval performance. Two applications of query difficulty estimation, namely guided pseudo relevance feedback (GPRF) and selective query refinement (SQR), are also proposed from both system and user perspectives. Experimental results show that both strategies further boost the retrieval performance.