Repeating average filter for noisy texture classification

Texture Classification: Are Filter Banks Necessary?

We question the role that large scale filter banks have traditionally played in texture classification. It is demonstrated that textures can be classified using the joint distribution of intensity values over extremely compact neighbourhoods (starting from as small as 3×3 pixels square), and that this outperforms classification using filter banks

Using filter banks in Convolutional Neural Networks for texture classification

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) f...

Texture classification with a biorthogonal directional filter bank

Classifying textures is a problem that has been considered by many researchers. Many of the high performance methods are based on extracting features from the textures and performing classification in the feature space. In this paper, we consider the application of a new directional filter bank (DFB) to the problem of texture classification. The DFB is used to provide a compact and efficient re...

Texture Analysis by Space Filter and Application to Foresttype Classification

Rotation Invariant Texture Classification Using Binary Filter Response Pattern (BFRP)

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (MR8) method, which extracts an 8-dimensional feature set from 38 filters, is one of state-of-the-art rotation invariant texture classification methods. However, this method has two limitations. First, it require a training stage to build a texton library, thus the accuracy depends on t...