Abstract
Deep Convolutional Neural Networks have set remarkable milestones in the field of computer vision, especially in image classification tasks. However, training a deep network is heavily depending on massive labeled data and expensive computation resource. A number of studies have shown that utilizing a pre-trained model for deep feature extraction can achieve excellent performance. While most of these methods only consider the features from fully connected layers, we delve deep into the intermediate convolution layers. We propose the Selected Multi-Scale Convolution feature (SMSC) for compact deep representations. A convolutional feature map selection and deep descriptor aggregation method are proposed, and a fusion method of the multi-layer features for compact representation is introduced. The experimental results on the known MIT-Indoor dataset have demonstrated the effectiveness and efficiency of the proposed method.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under grant number 61401154, by the Natural Science Foundation of Hebei Province under grant number F2016502101, and by the Fundamental Research Funds for the Central Universities under grant number 2015ZD20.
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Zhao, Z., Xu, G., Qi, Y. (2017). Multi-Scale Hierarchy Deep Feature Aggregation for Compact Image Representations. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_41
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DOI: https://doi.org/10.1007/978-3-319-54526-4_41
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