Abstract
In this paper, the analysis of similarity metrics used for performance evaluation of image retrieval frameworks is provided. Image retrieval based on similarity metrics obtains remarkable results in comparison with robust discrimination methods. Thus, the similarity metrics are used in matching process between visual query from user and descriptors of images in preprocessed collection. In contrast, the discrimination methods usually compare feature vectors computing distances between visual query and images in collections. In this research, a behavior of spline radial basis function used as metric for image similarity measurement is proposed and evaluated, comparing it with discrimination methods, particularly with general principal component analysis algorithm (GPCA). Spline radial basis function has been tested in image retrieval using a standard image collections, such as COIL-100. The obtained results using spline radial basis function report 88% of correct image retrieval avoiding a classification phase required in other well-known methods. The discussion of tests with designed Image Data Segmentation with Spline (IDSS) framework illustrates optimization and improvement of image retrieval process.
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Flores-Pulido, L. et al. (2011). Similarity Metric Behavior for Image Retrieval Modeling in the Context of Spline Radial Basis Function. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_39
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