Learning deep filter banks in parallel for texture recognition

Mining Sufficient Knowledge via Progressive Feature Fusion for Efficient Material Recognition

“…It contains 11,021 images (see Tables 2 and 3 for more details about these two datasets). [32] Describable texture dataset 74.70 FV-CNN [32] KTH-TIPS2b dataset 81.80 IFV + VGG [14] KTH-TIPS2b dataset 81.50 IFV + DFB [14] KTH-TIPS2a dataset 88.60 IFV + DFB [14] Flickr material dataset 82.70 IFV + DFB [14] Describable…”

“…As discussed above, deep learning features [1,2,14] can better characterize images of materials. Current mainstream CNN models usually improve performance by using a spatial dimensional layer.…”

“…Recently, deep learning features have played an important role in material recognition. Shahriari [14] used deep neural networks with variable depths to learn the scales, orientations, and resolutions of texture filter banks for effective material recognition. e corresponding computational cost was highly reduced, and their method could extract very deep features through distributed computing architectures.…”

“…ese deep learning methods [1,2,14] usually require massive amounts of data to train effective recognition models. Owing to the large cost of annotations, high-quality training samples are very scarce in the field of material recognition.…”

“…e abovementioned feature learning approaches [1,2,[10][11][12][13][14][15][16][17] have shown remarkable progress in material recognition. However, the knowledge achieved by a simple feature fusion method is insufficient to completely represent material images, and few studies have exploited the implicit but valuable knowledge learned through progressive feature fusion.…”