Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
An analysis of failed queries for web image retrieval
Journal of Information Science
Effective and efficient user interaction for long queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Proceedings of the 18th international conference on World wide web
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
From text question-answering to multimedia QA on web-scale media resources
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Learning concept importance using a weighted dependence model
Proceedings of the third ACM international conference on Web search and data mining
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Query term ranking based on dependency parsing of verbose queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the international conference on Multimedia
Assisted news reading with automated illustration
Proceedings of the international conference on Multimedia
Parameterized concept weighting in verbose queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multimedia answering: enriching text QA with media information
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
News contextualization with geographic and visual information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Learning concept bundles for video search with complex queries
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Oracle in Image Search: A Content-Based Approach to Performance Prediction
ACM Transactions on Information Systems (TOIS)
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Utilizing Related Samples to Enhance Interactive Concept-Based Video Search
IEEE Transactions on Multimedia
Learning to Recommend Descriptive Tags for Questions in Social Forums
ACM Transactions on Information Systems (TOIS)
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The use of image reranking to boost retrieval performance has been found to be successful for simple queries. It is, however, less effective for complex queries due to the widened semantic gap. This paper presents a scheme to enhance web image reranking for complex queries by fully exploring the information from simple visual concepts. Given a complex query, our scheme first detects the noun-phrase based visual concepts and crawls their top ranked images from popular image search engines. Next, it constructs a heterogeneous probabilistic network to model the relatedness between the complex query and each of its crawled images. The network seamlessly integrates three layers of relationships, i.e., the semantic-level, cross-modality level as well as visual-level. These mutually reinforced layers are established among the complex query and its involved visual concepts, by harnessing the contents of images and their associated textual cues. Based on the derived relevance scores, a new ranking list is generated. Extensive evaluations on a real-world dataset demonstrate that our model is able to characterize the complex queries well and achieve promising performance as compared to the state-of-the-art methods. Based on the proposed scheme, we introduce two applications: photo-based question answering and textual news visualization. Comprehensive experiments well validate the proposed scheme.