Recognition object-class

Dorkd, G. and Schmid, C. (2005) Object class recognition using discriminative local features. Technical Report RR-5497, INRIA-Rhone-Alpes, 2005. 

Fergus, R., Perona, P. and Zisserman, A. (2003) Object class recognition by unsupervised scale-invariant learning. Proceedings of Computer Vision and Pattern Recognition (CVPR), 2 264—271. 

Unfortunately, many classes of objects that humans can readily recognize are very difficult to characterize in this way. Object classes such as trees, chairs, or even handprinted characters do not have simple generic descriptions. One can characterize such classes by simplified, partial descriptions, but since these descriptions are usually incomplete, using them for object recognition will result in many errors. Even the individual parts of objects are often difficult to model many natural classes of shapes (e.g., clouds) or of surface textures (e.g., tree bark) are themselves difficult to characterize. 

The Bayesian approach is one of the probabilistic central parametric classification methods it is based on the consistent apphcation of the classic Bayes equation (also known as the naive Bayes classifier ) for conditional probabihty [34] to constmct a decision rule a modified algorithm is explained in references [105, 109, 121]. In this approach, a chemical compound C, which can be specified by a set of probability features (Cj,...,c ) whose random values are distributed through all classes of objects, is the object of recognition. The features are interpreted as independent random variables of an /w-dimensional random variable. The classification metric is an a posteriori probability that the object in question belongs to class k. Compound C is assigned to the class where the probability of membership is the highest. 

Mathematically, this means that one needs to assign portions of an 8-dimensionaI space to the three classes. A new sample is then assigned to the class which occupies the portion of space in which the sample is located. Supervised pattern recognition is distinct from unsupervised pattern recognition. In the latter one applies essentially clustering methods (Chapter 30) to classify objects into classes that are not known beforehand. In supervised pattern recognition, one knows the classes and has to decide in which of those an object should be classified. 

This is the simplest possible type of neuron, used here for didactic purposes and not because it is the configuration to be recommended. Let us suppose that for this isolated neuron w, = 1, Wj = 2 and 7=1. The line in Fig. 33.20 then gives the values of x, and Xj for which E = 7. All combinations of x, and Xj on and above the line will yield E > 7 and therefore lead to an output y, = 1 (i.e. the object is class K), all combinations below it toy, = 0. The procedure described here is equivalent to a method called the linear learning machine, which was one of the first supervised pattern recognition methods to be applied in chemometrics. It is further explained, including the training phase, in Chapter 44. 

Pattern recognition has been applied In many forms to various types of chemical data (1,2). In this paper the use of SIMCA pattern recognition to display data and detect outliers In different types of air pollutant analytical data Is Illustrated. Pattern recognition Is used In the sense of classification of objects Into sets with emphasis on graphical representations of data. Basic assumptions which are Implied In the use of this method are that objects In a class are similar and that the data examined are somehow related to this similarity. 

In the nineteen-seventies, new methods for pattern recognition have been developed by means of quantitative analogy models. The analogy analysis implies looking for regularities in the observations made. The individual items that are analyzed are called objects. The objects are alike that will be brought into the same class. In our case the number of classes would be two, the two barley cultivars (Etu,Tellus). The samples obtained from soil are used as test objects. 

The answers to these questions will usually be given by so-called unsupervised learning or unsupervised pattern recognition methods. These methods may also be called grouping methods or automatic classification methods because they search for classes of similar objects (see cluster analysis) or classes of similar features (see correlation analysis, principal components analysis, factor analysis). 

Since the recognition of supernovae as a separate class of astrophysical objects they have been proposed and used to measure the distance scale and the geometry of the universe. 

In supervised pattern recognition, a major aim is to define the distance of an object from the centre of a class. There are two principle uses of statistical distances. The first is to obtain a measurement analogous to a score, often called the linear discriminant function, first proposed by the statistician R A Fisher. This differs from the distance above in that it is a single number if there are only two classes. It is analogous to the distance along line 2 in Figure 4.26, but defined by... 

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