Fast Image Segmentation Using Multiple Features
DOI:
https://doi.org/10.32870/recibe.v13i1.358Keywords:
Segmentación de imagen, Algoritmo de cambio medio, Estimador de densidad del kernel (EDK)Abstract
Multi-Feature Fast Image Segmentation Multi-feature segmentation is superior to one-dimensional grayscale-based approaches. The Mean Shift (MS) algorithm is commonly used for this task. Despite its promising results, MS remains computationally prohibitive for segmentation scenarios where the feature map is composed of multidimensional features. The proposed approach considers a two-dimensional feature map that includes the grayscale value and the local variance of each image pixel. To reduce computational cost, the classical MS algorithm is modified to operate on a smaller number of points. Under this scheme, two sets of elements are distinguished: involved data (the reduced set of data points considered during the MS operation) and non-involved data (the remaining available data). Unlike the classical MS algorithm, which employs Gaussian kernels, the proposed approach performs feature-map estimation using the more accurate Epanechnikov kernel function. Once the MS results are obtained, they are generalized to include the unused data. Each unused feature is assigned to the cluster of the nearest utilized data point. Finally, clusters containing fewer features are merged with neighboring clusters. The proposed segmentation method was compared with other state-of-the-art algorithms using the Berkeley Image Segmentation Database. Experimental results confirm that the proposed scheme produces segmented images with 50% higher visual perception quality compared to existing approaches.References
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