The text associated with images provides valuable semantic meanings about image content that can hardly be described by low-level visual features. In this paper, we propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of textual features along with visual ones. In contrast to the classical Bag-of-Words approach which simply relies on term frequencies, we propose a novel textual descriptor, namely the Histogram of Textual Concepts (HTC), which accounts for the relatedness of semantic concepts in accumulating the contributions of words from the image caption toward a dictionary. In addition to the popular SIFT-like features, we also evaluate a set of mid-level visual features, aiming at characterizing the harmony, dynamism and aesthetic quality of visual content, in relationship with affective concepts. Finally, a novel selective weighted late fusion (SWLF) scheme is proposed to automatically select and weight the scores from the best features according to the concept to be classified. This scheme proves particularly useful for the image annotation task with a multi-label scenario. Extensive experiments were carried out on the MIR FLICKR image collection within the ImageCLEF 2011 photo annotation challenge. Our best model, which is a late fusion of textual and visual features, achieved a MiAP (Mean interpolated Average Precision) of 43.69% and ranked 2nd out of 79 runs. We also provide comprehensive analysis of the experimental results and give some insights for future improvements.