IBM Research at Image CLEF 2015: Medical Clustering Task.
Suman Sedai, Xi Liang, Mani Abedini, Qiang Chen, Rajib Chakravorty, Rahil Garnavi · CLEF (Working Notes) · 2015
In this paper, we present the learning strategies and fea- ture extraction techniques that were applied by the IBM Research Aus- tralia 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 cate- gories. Our proposed methodology and details of experiments for each submitted run has been discussed in this paper, followed by nal 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 sher vector, various SVM kernels, and an eective fusion methodol- ogy, to ensure high classication accuracy. Comprehensive experiments demonstrate the eectiveness 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 rst place in the competition.