In this paper, we present the learning strategies and feature extraction techniques that were applied by the IBM Research Australia team to the Medical Clustering challenge of ImageCLEF 2015. The challenge is to automatically annotate and categorize X-ray images into head-neck, body, upper-limb, lower-limb and foreign object categories. Our proposed methodology and details of experiments for each submitted run has been discussed in this paper, followed by final results provided by the competition organizers. The key components used in our submissions are based on sparse coding of SIFT, local binary patterns and multi-scale local binary patterns with spatial pyramid, advanced fisher vector, various SVM kernels, and an eéctive fusion methodology, to ensure high classification accuracy. Comprehensive experiments demonstrate the effectiveness of the proposed system. Six out of the ten submissions of IBM Research were among the top 10 best results, where two of our submissions outperformed all other submissions, therefore the team has achieved the first place in the competition.