The dissimilarity representation for pattern recognition, a tutorial

The dissimilarity representation for non-Euclidean pattern recognition, a tutorial

Local gradient pattern - A novel feature representation for facial expression recognition

Many researchers adopt Local Binary Pattern for pattern analysis. However, the long histogram created by Local Binary Pattern is not suitable for large-scale facial database. This paper presents a simple facial pattern descriptor for facial expression recognition. Local pattern is computed based on local gradient flow from one side to another side through the center pixel in a 3x3 pixels region...

The dissimilarity space: Bridging structural and statistical pattern recognition

Human experts constitute pattern classes of natural objects based on their observed appearance. Automatic systems for pattern recognition may be designed on a structural description derived from sensor observations. Alternatively, training sets of examples can be used in statistical learning procedures. They are most powerful for vectorial object representations. Unfortunately, structural descr...

Dissimilarity between two skeletal trees in a context

Skeletal trees are commonly used in order to express geometric properties of the shape. Accordingly, tree edit distance is used to compute a dissimilarity between two given shapes. We present a new tree edit based shape matching method which uses a recent coarse skeleton representation. The coarse skeleton representation allows us to represent both shapes and shape categories in the form of dep...

The Dissimilarity Representation for Structural Pattern Recognition

The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural pattern recognition aims to find explicit procedures that mimic the learning and classification made by human experts in well-defined and restricted areas o...