Automatic cleaning and segmentation of web images based on colors to build learning databases
This article proposes a method to segment Internet images, that is, a group of images corresponding to a specific object (the query) containing a significant amount of irrelevant images. The segmentation algorithm we propose is a combination of two distinct methods based on color. The first one considers all images to classify pixels into two sets: object pixels and background pixels. The second method segments images individually by trying to find a central object. The final segmentation is obtained by intersecting the results from both. The segmentation results are then used to re-rank images and display a clean set of images illustrating the query. The algorithm is tested on various queries for animals, natural and man-made objects, and results are discussed, showing that the obtained segmentation results are suitable for object learning.
web images semantics automatic segmentation sorting images