This paper shows how semantic attribute features can be used to improve object classification performance. The semantic attributes used fall into five groups: scene (e.g. ‘road’), colour (e.g. ‘green’), part (e.g. ‘face’), shape (e.g. ‘box’), and material (e.g. ‘wood’). We train classifiers from representative images for 60 semantic attributes. We first assess the accuracy of the individual classifiers, and show that they can be used to predict semantic annotations for test images. We then use output from the set of trained classifiers to create a new low-dimensional image representation. Experiments on data from the PASCAL VOC challenge show that the semantic attribute features achieve an object classification performance close to that of high-dimensional bag-of-words features, and that using a combination of semantic attribute features and bag-of-words features gives a better classification performance than using either feature set alone.
['Yu Su', 'Moray Allan', 'Frédéric Jurie']
Improving object classification using semantic attributes