The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Making large-scale support vector machine learning practical
Advances in kernel methods
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
OVID: Design and Implementation of a Video-Object Database System
IEEE Transactions on Knowledge and Data Engineering
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
MM '08 Proceedings of the 16th 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
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Localizing objects while learning their appearance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Learning from search engine and human supervision for web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Learning to judge image search results
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Interactive experiments in object-based retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Visual reranking with local learning consistency
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Object Retrieval Using Visual Query Context
IEEE Transactions on Multimedia
Distributed cosegmentation via submodular optimization on anisotropic diffusion
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Salient object detection by composition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Image search reranking has been an active research topic in recent years to boost the performance of the existing web image search engine which is mostly based on textual metadata of images. Various approaches have been proposed to rerank images for general queries and argue that, they may not necessarily be optimal for queries in specific domain, e.g., object queries, since the reranking algorithms are operated on whole images, instead of the relevant parts of images. In this paper, we propose a novel bag-of-objects retrieval model for image search reranking of object queries. Firstly, we employ a common object discovery algorithm to discover query-relevant objects from the search results returned by text-based image search engine. Then, the query and its result images are represented as a language model on the query relevant object vocabulary, based on which the ranking function can be derived. As the common object discovery is unreliable and may introduce noises, we propose to incorporate the attributes of the discovered objects, e.g., size, position, etc., into the ranking function through a linear model, and the weights on the object attributes can be learned. The experiments on two subsets of Web Queries dataset comprising object queries demonstrate that our approach can significantly outperform the existing reranking methods on object queries.