STATISTICAL DATA ANALYSIS TOOLS IN IMAGE CLASSIFICATION METHODS BASED ON THE DESCRIPTION AS A SET OF BINARY DESCRIPTORS OF KEY POINTS
DOI:
https://doi.org/10.15588/1607-3274-2021-4-6Keywords:
computer vision, key point, descriptor, data aggregation, statistical distribution, significance of classification decision, processing speed.Abstract
Context. Modern computer vision systems require effective classification solutions based on the research of the the processed data nature. Statistical distributions are currently the perfect tool for representing and analyzing visual data in image recognition systems. If the description of a recognized object is represented by a set of vectors, the statistical apparatus becomes fundamental for making a classification decision. The study of data distributions in the feature blocks systems for key point descriptors has shown its effectiveness in terms of achieving the necessary quality of classification and processing speed. There is a need for in-depth study of the descriptor sets statistical properties in terms of the main aspect – the multidimensional data separation for classification. This task becomes especially important for constructing new effective feature spaces, for example, by aggregating a set of descriptors by their constituent components, including individual bits. To do this, it is natural to use the apparatus of statistical criteria designed to compare the parameters of the distribution of the studied samples. Despite the widespread use and applied effectiveness of the feature descriptors apparatus for image classification, the statistical basis of these methods in their implementation in aggregate visual data systems and the choice of effective means to assess their effectiveness for distinguishing real images in application databases remains insufficiently studied.
Objective. Development of an effective images classification method by introducing aggregate statistical features for the description components.
Method. A metric image classifier based on feature aggregation for a set of image descriptors using statistical criteria for assessing the classification decision significance is proposed.
Results. The synthesis of the classification method on the basis of the introduction of aggregated statistical features for a set of image description descriptors is carried out. The efficiency and effectiveness of the developed classifier are confirmed. On examples of application of a method for system of real images features its efficiency is experimentally estimated.
Conclusions. The study makes possible to evaluate the applied effectiveness of the key points descriptors apparatus and build on its basis an aggregate features system for the effective visual objects classification implementation. Our research has shown that the available information in the form of a bit descriptors representation is sufficient for a significant statistical distinction between visual objects descriptions. Analysis of pairs and other blocks for descriptor bits provides a promising opportunity to reduce processing time.
The scientific novelty of the study is the development of a method of image classification based on an integrated statistical features system for structural description, confirmation of the effectiveness of the method and the importance of the created features classification system in the image database.
The practical significance of the work is to confirm the efficiency of the proposed methods on the real image descriptions examples.
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