In CLEF (Notebook Papers/Labs/Workshop), 2010

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Our approach to the ImageCLEF 2010 tasks is based on Histogram of Oriented Gradients descriptors (HOG) and Okapi BM25 based text retrieval. We extracted feature vectors to describe the visual content of an image region or the entire image. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image regions. To represent each image with only one vector we computed a Bag-of-Words (BOW) model from GMM probabilities of HOG descriptors. We trained linear regression classificators for the Photo Annotation task. To improve our textual ranking in the Wikipedia MM task we successfully expanded the textual query based on the visual ranking.