Image Aesthetic Assessment via Deep Semantic Aggregation
We proposed a new parameterized pooling layer - Ordered Weighted Averaging Layer to aggregate features from multi-column networks. We first sort features along specific dimension and multiply them with trainable weights to form a aggregated feature. The parameters of the network are trained by end-to-end back-propagation technique. Results on the standard benchmark of aesthetic quality assessment shows the effectiveness of our approach.
If you find Ordered Weighted Averagin Layer useful in your research, please consider citing:
Image Aesthetic Assessment via Deep Semantic Aggragation
Kung-Hung Lu, Kuang-Yu Chang and Chu-Song Chen
IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016
Performance comparison of different algorithms on AVA dataset. The table shows the accuracy(%) of standard testing set.
| Method | AVA Dataset(%) |
|---|---|
| Murry et al. | 68.0 |
| SCNN | 71.2 |
| AVG-SCNN | 69.9 |
| DCNN | 73.3 |
| RDCNN | 74.5 |
| AlexNet | 72.3 |
| DMA-Net-ImgFu | 75.4 |
| Ours | 78.6 |
Please refer to Caffe prerequisites
Note: we are now just providing CPU version, the GPU version is coming soon...
example :
layer {
name: "aggregation"
bottom: "1st_pool5"
bottom: "2nd_pool5"
bottom: "3rd_pool5"
bottom: "4th_pool5"
type: "OrderedWeighted"
top: "aggregation"
ordered_weighted_param {
OrderOp: DEX // Sorted in ascending order or descending order
positive: true // Force weights to be positive
axis: 1 // The axis to aggregate
weight_filler {
type: "gaussian"
mean: 0.5
std: 0.1
}
}
}
comming soon...
Please feel free to leave suggestions or comments to Kung-Hung Lu ([email protected]), Kuang-Yu Chang ([email protected]) and Chu-Song Chen ([email protected])