Training sets of images for object recognition are the pillars on which classifiers base their performances. We have built a framework to support the entire process of image and textual retrieval from search engines, which, giving an input keyword, performs a statistical and a semantic analysis and automatically builds a training set. We have focused our attention on textual information and we have explored, with several experiments, three different approaches to automatically discriminate between positive and negative images: keyword position, tag frequency and semantic analysis. We present the best results for each approach.
Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags
Zeni Nicola;Ferrario Roberta;
2015
Abstract
Training sets of images for object recognition are the pillars on which classifiers base their performances. We have built a framework to support the entire process of image and textual retrieval from search engines, which, giving an input keyword, performs a statistical and a semantic analysis and automatically builds a training set. We have focused our attention on textual information and we have explored, with several experiments, three different approaches to automatically discriminate between positive and negative images: keyword position, tag frequency and semantic analysis. We present the best results for each approach.File in questo prodotto:
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