International Journal of Computer Vision
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Order-based fitness functions for genetic algorithms applied to relevance feedback
Journal of the American Society for Information Science and Technology
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Improving image retrieval performance by inter-query learning with one-class support vector machines
Neural Computing and Applications
Nearest-Prototype Relevance Feedback for Content Based Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Evidence Combination for Multi-Point Query Learning in Content-Based Image Retrieval
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A new framework to combine descriptors for content-based image retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying logistic regression to relevance feedback in image retrieval systems
Pattern Recognition
Adaptive salient block-based image retrieval in multi-feature space
Image Communication
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A relevance feedback CBIR algorithm based on fuzzy sets
Image Communication
Fast Query Point Movement Techniques for Large CBIR Systems
IEEE Transactions on Knowledge and Data Engineering
A Novel Evolutionary Approach for Optimizing Content-Based Image Indexing Algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A human-oriented image retrieval system using interactive genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Content-based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except the own content of the images, which is usually represented as a feature vector extracted from low-level descriptors. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and distance-based learning in an attempt to reduce the existing gap between the high level semantic content of the images and the information provided by their low-level descriptors. In particular, a framework which is independent from the particular features used is presented. The effect of different crossover strategies and mutation rates is evaluated, and the performance of the technique is compared to that of other existing algorithms, obtaining considerably better and very promising results.