THE STUDY OF STATISTICAL PROPERTIES OF THE MODEL BLOCK REPRESENTATION FOR SET OF DESCRIPTORS OF KEY POINTS OF IMAGES
DOI:
https://doi.org/10.15588/1607-3274-2019-2-11Keywords:
computer vision, structural image recognition, multiple key points, BRISK descriptors, descriptive relevance, block representation, statistical distribution, chi-square test, Renyi divergence, sign criterion, significance of the difference of descriptions.Abstract
Context. The multidimensional nature of the processed data in modern computer vision systems requires new approaches to theconstruction of effective feature spaces that simplify processing by summarizing the available information. Structural methods of
image recognition use descriptions of visual objects in the form of sets of key point descriptors as sets of numerical vectors of high
dimension. The main tool for reducing the dimension is the presentation of data in the form of a system of their blocks and a statistical
study of such data structures, which in terms of recognition should display the generalized properties of an object as a set of its
fragments. In this connection, there is problem of studying the features of the applied application and the characteristics of the block
representation model in the aspect of its use to determine the relevance of descriptions and classify data within the base of reference
images.
Objective. Perform statistical estimation of the importance of making classification decisions on the basis of the calculation of
the relevance of object descriptions for the model of block representation of descriptors of key points of images.
Method. Methods are proposed for distinguishing descriptions based on the application of the block representation model of the
data descriptors of key points of images using the criteria of mathematical statistics and information theory tools.
Results. The main result of the article is the confirmation that the use of classical statistical criteria for analyzing empirical data
in the form of structural descriptions of images makes it possible to determine the quality factor of the constructed feature space for
distinguishing visual objects when they are recognized in computer vision systems. The introduction of the block representation
model and statistical analysis for the values of descriptors of key features of images contributes to the efficiency of the process of
recognizing visual objects, which is confirmed by the improvement in the level of difference by increasing the fragment size in the
constructed description chain structure.
Conclusions. The use of a variety of statistical criteria gave an identical effect on the significance of the differences in the empirical
descriptions of visual objects in the constructed feature space, which underlines the objectivity of the study. The implemented
model of block representation of data retains the distinguishing properties of a structural description with the effect of a significant
improvement in the speed of classification decision making.
The scientific novelty of the study is the improvement and statistical substantiation of models for making decisions about the
class of visual objects based on the calculation of the relevance of their descriptions with references using a block representation of
descriptors of key points of images.
The practical significance of the work is confirming the advisability of introducing a block structure for the block description of
an object as an effective approach to solving the problem of recognition with examples of images for implementation in computer
vision systems.
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