Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags

Training sets of images for object recognition are the pillars on which classiers base their performances. We have built a framework to support the entire process of image and textual retrieval from search engines such that, giving an input keyword, calculate the statistical analysis, the semantic analysis and automatically build a training set. We have focused our attention on textual information and we have explored, with several experiments, three dierent approaches to automatically discriminate between positive and negative images: keyword position, tag frequency and semantic analysis. We present the best results for each approach.