C4.5: programs for machine learning
C4.5: programs for machine learning
Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of accessing nonmatching documents on relevance feedback
ACM Transactions on Information Systems (TOIS)
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
S3: similarity search in CAD database systems
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Improving Adaptable Similarity Query Processing by Using Approximations
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
Similarity Search for Adaptive Ellipsoid Queries Using Spatial Transformation
Proceedings of the 27th International Conference on Very Large Data Bases
Relevance Feedback and Category Search in Image Databases
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
IEEE Transactions on Multimedia
Leveraging non-relevant images to enhance image retrieval performance
Proceedings of the tenth ACM international conference on Multimedia
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
On scalability of active learning for formulating query concepts
Proceedings of the 1st international workshop on Computer vision meets databases
Relevance feedback using adaptive clustering for image similarity retrieval
Journal of Systems and Software
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Query by example for geographic entity search with implicit negative feedback
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
| Hi-index | 0.00 |
Many modern database applications require content-based similarity search capability in numeric attribute space. Further, users' notion of similarity varies between search sessions. Therefore online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the similarity metric, without taking into account the information about non-relevant (or unsatisfactory) items. Consequently items in database close to non-relevant ones continue to be retrieved in further iterations. In this paper a robust technique is proposed to incorporate non-relevant information to efficiently discover the feasible search region. A decision surface is determined to split the attribute space into relevant and nonrelevant regions. The decision surface is composed of hyperplanes, each of which is normal to the minimum distance vector from a nonrelevant point to the convex hull of the relevant points. A similarity metric, estimated using the relevant objects is used to rank and retrieve database objects in the relevant region. Experiments on simulated and benchmark datasets demonstrate robustness and superior performance of the proposed technique over existing adaptive similarity search techniques.