Introduction to algorithms
Vector approximation based indexing for non-uniform high dimensional data sets
Proceedings of the ninth international conference on Information and knowledge management
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Increasing retrieval efficiency by index tree adaptation
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Relevance Feedback and Category Search in Image Databases
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
An effective region-based image retrieval framework
Proceedings of the tenth ACM international conference on Multimedia
Kernel VA-files for relevance feedback retrieva
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
The Active Vertice method: a performant filtering approach to high-dimensional indexing
Data & Knowledge Engineering
Kernel Vector Approximation Files for Relevance Feedback Retrieval in Large Image Databases
Multimedia Tools and Applications
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Using high dimensional indexes to support relevance feedback based interactive images retrieval
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
OCRS: an interactive object-based image clustering and retrieval system
Multimedia Tools and Applications
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Speed up interactive image retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
A fast pivot-based indexing algorithm for metric spaces
Pattern Recognition Letters
Aspect-based relevance learning for image retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Relevance feedback for sketch retrieval based on linear programming classification
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
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Relevance feedback is often used in refining similarity retrievals in image and video databases. Typically this involves modification to the similarity metrics based on the user feedback and recomputing a set of nearest neighbors using the modified similarity values. Such nearest neighbor computations are expensive given that typical image features, such as color and texture, are represented in high dimensional spaces. Search complexity is a ciritcal issue while dealing with large databases and this issue has not received much attention in relevance feedback research. Most of the current methods report results on very small data sets, of the order of few thousand items, where a sequential (and hence exhaustive search) is practical. The main contribution of this paper is a novel algorithm for adaptive nearest neigbor computations for high dimensional feature vectors and when the number of items in the databse is large. The proposed method exploits the correlations between two consecutive nearest neighbor searches when the underlying similarity metric is changing, and filters out a significant number of candidates ina two stage search and retrieval process, thus reducing the number of I/O accesses to the database. Detailed experimental results are provided using a set of about 700,000 images. Comparision to the existing method shows an order of magnitude overall imporovement.