Two-scale image retrieval with significant meta-information feedback
Proceedings of the 13th annual ACM international conference on Multimedia
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Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
The Story Picturing Engine---a system for automatic text illustration
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An unsupervised method for clustering images based on their salient regions of interest
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
SmartLabel: an object labeling tool using iterated harmonic energy minimization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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ACM Transactions on Information Systems (TOIS)
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Multimedia Tools and Applications
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Proceedings of the international workshop on Workshop on multimedia information retrieval
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SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Leveraging user query log: toward improving image data clustering
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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Sparse Multiscale Patches for Image Processing
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ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
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Fuzzy Sets and Systems
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IEEE Transactions on Circuits and Systems for Video Technology
Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees
Expert Systems with Applications: An International Journal
Image retrieval using fuzzy relevance feedback and validation with MPEG-7 content descriptors
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
A statistical image retrieval method using color invariant
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
VISTO: A new CBIR system for vector images
Information Systems
Efficient image retrieval in DCT domain by hypothesis testing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Combining CBIR and NLP for multilingual terminology alignment and cross-language image indexing
YIWCALA '10 Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Maximum normalized spacing for efficient visual clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Connected component in feature space to capture high level semantics in CBIR
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
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SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
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PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.