We tested our image classification methodology in the photo-annotation task of the ImageCLEF competition [Nowak, 2010] using a visual-only approach performing automated labeling. Our labeling process consisted of three phases: 1) feature extraction using color histogramming and using a novel method of structural description, that was exploited in a statistical manner only; 2) classification using Linear Discriminant (LD) or Average-Retrieval Rank (ARR) methods that provided the confidence (scalar) values, which were then thresholded to obtain the binary values; 3) eliminating labels (setting binary values to 0) on the testing set thereby exploiting the calculated joint-probabilities for pairs of concepts from the training set. The results show that our present system performs better on 'whole-image' labels than on object labels.