E-PixelHop: An Enhanced PixelHop Method for Object Classification

• URL: http://arxiv.org/abs/2107.02966v1
• Date: Wed, 7 Jul 2021 01:22:12 GMT
• Title: E-PixelHop: An Enhanced PixelHop Method for Object Classification
• Authors: Yijing Yang, Vasileios Magoulianitis and C.-C. Jay Kuo
• Abstract summary: We propose an enhanced solution for object classification, called E-PixelHop, in this work. E-PixelHop consists of the following steps. We conduct pixel-level classification at each hop with various receptive fields. Forth, pixel-level decisions from each hop and from each color subspace are fused together for image-level decision.
• Score: 29.062926577155885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on PixelHop and PixelHop++, which are recently developed using the successive subspace learning (SSL) framework, we propose an enhanced solution for object classification, called E-PixelHop, in this work. E-PixelHop consists of the following steps. First, to decouple the color channels for a color image, we apply principle component analysis and project RGB three color channels onto two principle subspaces which are processed separately for classification. Second, to address the importance of multi-scale features, we conduct pixel-level classification at each hop with various receptive fields. Third, to further improve pixel-level classification accuracy, we develop a supervised label smoothing (SLS) scheme to ensure prediction consistency. Forth, pixel-level decisions from each hop and from each color subspace are fused together for image-level decision. Fifth, to resolve confusing classes for further performance boosting, we formulate E-PixelHop as a two-stage pipeline. In the first stage, multi-class classification is performed to get a soft decision for each class, where the top 2 classes with the highest probabilities are called confusing classes. Then,we conduct a binary classification in the second stage. The main contributions lie in Steps 1, 3 and 5.We use the classification of the CIFAR-10 dataset as an example to demonstrate the effectiveness of the above-mentioned key components of E-PixelHop.

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