A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting the structuring element for morphological texture classification
Pattern Analysis & Applications
Applying logistic regression to relevance feedback in image retrieval systems
Pattern Recognition
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
Relevance feedback based on genetic programming for image retrieval
Pattern Recognition Letters
An improved distance-based relevance feedback strategy for image retrieval
Image and Vision Computing
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CBIR (content-based image retrieval) systems attempt to allow users to perform searches in large picture repositories. In most existing CBIR systems, images are represented by vectors of low level features. Searches in these systems are usually based on distance measurements defined in terms of weighted combinations of the low level features. This paper presents a novel approach to combining features when using multi-image queries consisting of positive and negative selections. A fuzzy set is defined so that the degree of membership of each image in the repository to this fuzzy set is related to the user's interest in that image. Positive and negative selections are then used to determine the degree of membership of each picture to this set. The system attempts to capture the meaning of a selection by modifying a series of parameters at each iteration to imitate user behavior, becoming more selective as the search progresses. The algorithm has been evaluated against four other representative relevance feedback approaches. Both the performance and usability of the five CBIR systems have been studied. The algorithm presented is easy to use and yields the highest performance in terms of the average number of iterations required to find a specific image. However, it is computationally more expensive and requires more memory than two of the other techniques.