Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.