Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiple instance learning with generalized support vector machines
Eighteenth national conference on Artificial intelligence
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
The Journal of Machine Learning Research
Central object extraction for object-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Local image representations using pruned salient points with applications to CBIR
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Graph-based multiple-instance learning for object-based image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Localized content-based image retrieval using semi-supervised multiple instance learning
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
ShotTagger: tag location for internet videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Multiple-Instance learning via random walk
ECML'06 Proceedings of the 17th European conference on Machine Learning
Multi-graph multi-instance learning for object-based image and video retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Object categorization based on a supervised mean shift algorithm
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
On the informativeness of asymmetric dissimilarities
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Robust subspace discovery via relaxed rank minimization
Neural Computation
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Classic Content-Based Image Retrieval (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define Localized Content-Based Image Retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. Many classic CBIR systems use relevance feedback to obtain images labeled as desirable or not desirable. Yet, these labeled images are typically used only to re-weight the features used within a global similarity measure. In this paper we present a localized CBIR system, acciop, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and re-weight the features, and then to rank images in the database using a similarity measure that is based upon individual regions within the image. We evaluate our system using a five-category natural scenes image repository, and benchmark data set, SIVAL, that we have constructed with 25 object categories.