Abstract
Computer vision, a specialization of machine learning aims to train machines to learn and interpret visual content based on its features. Many CNN based architectural models are developed to classify fashion images. The basic accuracy loss or misclassification occurs in similar fashion items. The reason for this is the minute features may not be extracted efficiently by existing models. Therefore, in the proposed study we utilize the feature concatenation property, knowledge consolidation and inductive feature transfer of DenseNet architecture for categorization of fashion MNIST dataset. The performance analysis is done by generating a confusion matrix. Further, we compare the performance of proposed architecture with the state of the art image classification architectures in literature. The analysis indicates that our proposed system outperforms the existing architectures.
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Pillai, R.S., Sreekumar, K. (2021). Classification of Fashion Images Using Transfer Learning. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_32
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DOI: https://doi.org/10.1007/978-981-15-5788-0_32
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