In this paper, we focus on one of the ImageCLEF tasks that LIRIS-Imagine research group participated: visual concept detection and annotation. For this task, we firstly propose two kinds of textual features to extract semantic meanings from text associated to images: one is based on semantic distance matrix between the text and a semantic dictionary, and the other one carries the valence and arousal meanings by making use of the Affective Norms for English Words (ANEW) dataset. Meanwhile, we investigate efficiency of different visual features including color, texture, shape, high level features, and we test four fusion methods to combine various features to improve the performance including min, max, mean and score. The results have shown that combination of our textural features and visual features can improve the performance significantly.