2019 IEEE International Conference on Image Processing (ICIP) 2019
DOI: 10.1109/icip.2019.8803640
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Multi-Level Texture Encoding and Representation (Multer) Based on Deep Neural Networks

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Cited by 21 publications
(29 citation statements)
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“…For the first component, we investigated the effects of a) normalization , b) sum-to-one constraint across bins, and c) location of histogram layer. Previous works incorporated batch normalization of their convolutional features before fusing their texture features (i.e., concatenation, weighting) [20,43,46]. We wanted to investigate whether the impact of batch normalization for our new layer would improve results similar to existing "encoding layers."…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations
“…For the first component, we investigated the effects of a) normalization , b) sum-to-one constraint across bins, and c) location of histogram layer. Previous works incorporated batch normalization of their convolutional features before fusing their texture features (i.e., concatenation, weighting) [20,43,46]. We wanted to investigate whether the impact of batch normalization for our new layer would improve results similar to existing "encoding layers."…”
Section: Methodsmentioning
confidence: 99%
“…Previous histogram-based features used lower level features (e.g., edges [16] for edge histogram descriptors) and learning histograms of convolutional features earlier in the network may exploit more texture information. Training Details A similar training procedure from [20,43] was used in this work. For each dataset, the image is resized to 256×256 and a random crop of 80 to 100 % of the image was extracted with a random aspect ratio of 3/4 to 4/3.…”
Section: Methodsmentioning
confidence: 99%
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“…To combine both low-and high-level CNN features, we propose a multi-level texture encoding and representation network (MuLTER) on top of our previous work [49], whose architecture is shown in Fig. 6 and Table II.…”
Section: Proposed Learning-based Methods For Materials Surface Charac...mentioning
confidence: 99%