International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Spectral covariance and fuzzy regions for image indexing
Machine Vision and Applications
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
A Weighted Distance Approach to Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Representation of images for classification with independent features
Pattern Recognition Letters
Probability-based fusion of information retrieval result sets
Artificial Intelligence Review
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It is a common way to process different image features independently in order to measure similarity between images. Color and texture are the common ones to use for searching in natural images. In [10] a technique to combine color and texture features based on a particular query-image in order to improve retrieval efficiency was proposed. Weighted linear combination of color and texture metrics was considered as a mixed-metrics. In this paper the mixed-metrics with different weights are compared to pure color and texture metrics and widely used CombMNZ data fusion algorithm. Experiments show that proposed metrics outperform CombMNZ method in some cases, and have close results in others.