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Correcting Misclassified Image Features with Convolutional Coding
Ye-Ji Mun(문예지),Nayoung Kim(김나영),Jieun Lee(이지은),Je-Won Kang(강제원) 한국방송·미디어공학회 2018 한국방송공학회 학술발표대회 논문집 Vol.2018 No.11
The aim of this study is to rectify the misclassified image features and enhance the performance of image classification tasks by incorporating a channel-coding technique, widely used in telecommunication. Specifically, the proposed algorithm employs the error-correcting mechanism of convolutional coding combined with the convolutional neural networks (CNNs) that are the state-of-the-arts image classifiers. We develop an encoder and a decoder to employ the error-correcting capability of the convolutional coding. In the encoder, the label values of the image data are converted to convolutional codes that are used as target outputs of the CNN, and the network is trained to minimize the Euclidean distance between the target output codes and the actual output codes. In order to correct misclassified features, the outputs of the network are decoded through the trellis structure with Viterbi algorithm before determining the final prediction. This paper demonstrates that the proposed architecture advances the performance of the neural networks compared to the traditional one-hot encoding method.
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