Abstract
Image retrieval can be done on the basis of features, either low-level (content-based image retrieval) or high-level (semantic-based image retrieval). Since semantic-based features rely on low-level ones, in this chapter, we present our research in the field of content-based image retrieval (CBIR) using color and texture features. The research was effectuated on medical images from digestive tract. The image color information is represented by means of color histograms resulting from the transformation of the RGB color space to HSV color space and the quantization at 166 colors. In order to represent the color texture, we used the method based on co-occurrence matrices. To compute the images dissimilarity, the histogram intersection has been used for color and the Euclidian distance for color texture. The experiments have demonstrated a good quality of the CBIR process on this type of images by using these methods.
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Stanescu, L., Burdescu, D.D., Brezovan, M., Mihai, C.G. (2012). Content-Based Image Retrieval in Medical Images Databases. In: Creating New Medical Ontologies for Image Annotation. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1909-9_2
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DOI: https://doi.org/10.1007/978-1-4614-1909-9_2
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