Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Perspective View on Visual Information Retrieval Systems
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Performance evaluation and optimization for content-based image retrieval
Pattern Recognition
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Content Based Image Retrieval Using Color, Texture and Shape Features
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Self organizing natural scene image retrieval
Expert Systems with Applications: An International Journal
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Feature extraction is a key issue in Content Based Image Retrieval (CBIR). In the past, a number of describing features have been proposed in literature for this goal. In this work a feature extraction and classification methodology for the retrieval of natural images is described. The proposal combines fixed and random extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (form the co-occurrence) of a sub-image extracted from the three channels: H, S and I. A K -MEANS algorithm and a 1-NN classifier are used to build an indexed database of 300 images. One of the advantages of the proposal is that we do not need to manually label the images for their retrieval. After performing our experimental results, we have observed that in average image retrieval using images not belonging to the training set is of 80.71% of accuracy. A comparison with two similar works is also presented. We show that our proposal performs better in both cases.