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A Generic Moment Invariants Based Supervised Learning Framework for Classification Using Partial Object Information

We present a novel classification scheme which uses partial object information that is selected adaptively using modified distance transform and represented as moment invariants (Hu moments) to compensate for scale, translation and rotational transformation(s). The moment invariants of different parts of an object are learned using AdaBoost algorithm [1]. The classifier obtained using the proposed scheme is able to handle changes in illumination, pose, and varying inter-class and intra-class attributes. Partial information based classification shows robustness against object articulations, clutters, and occlusions. The first contribution of our proposed method is an adaptive selection of partial object information using modified distance transform that attempts to extract contours along with its neighborhood information in the form of blocks. Secondly, our proposed method is invariant to scaling, translation and rotation, and reliably classifies occluded objects using fractional information. Our proposed method achieved better detection and classification rate compared to other state-of-the-art schemes.