Characterizing objects with SIKA features for multiclass classification

This paper presents a novel approach for multiclass classification by fusion of KAZE and Scale Invariant Feature Transform (SIFT) features followed by Minimal Complexity Machine (MCM) as the classifier. Unlike the existing features, the paper proposes a new feature SIKA to represent characteristics of an object, as opposed to just forming a compendium of interest points in an image to represent the object characteristics. Further we append a strong and lightweight classifier, MCM to the technique. The resulting classifier easily outperforms existing techniques based on handcrafted features. Two new scores Keypoint Overlap Score (KOS) and Mean Keypoint Overlap Score (MKOS) have also been proposed as part of this work which are useful in establishing the strength of features for object representation.

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