Part of Image Tagging
نویسندگان
چکیده
In this project we undertake the task of semantic labeling of images. An input image is broken into blocks each of which is assigned one of k categories. Inspired by the part-ofspeech tagging techniques used in language processing, we design an discriminative HMM/CRF based tagger to tag the blocks. We learn subcategories for each tag category in an unsupervised manner, and obtain improved performances for some categories. The HMM/CRF tagger also is slightly better compared to a classifier which tags each block independently. To classify the image we describe it as multiscale spatial distribution of the tags. This representation leads to about 2.3% improvement using the ground truth tags and about 1% improvement using the tagger, compared to a recent result of Vogel et.al [1].
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