STATISTICAL MEASURES FOR COMPUTATION OF THE IMAGE RELEVANCE OF VISUAL OBJECTS IN THE STRUCTURAL IMAGE CLASSIFICATION METHODS

2018 ◽  
Vol 77 (12) ◽  
pp. 1041-1053 ◽  
Author(s):  
S. V. Gadetska ◽  
V. О. Gorokhovatsky
Keyword(s):  
Image Classification ◽  
Classification Methods ◽  
Visual Objects ◽  
Statistical Measures ◽  
Structural Image

scholarly journals Application of data hashing tools to accelerate classification decisions in structural image recognition methods

2021 ◽  
Vol 5 (2) ◽  
pp. 13-20
Author(s):  
Volodymyr Gorokhovatsky ◽  
Nataliia Vlasenko ◽  
Mykhailo Rybalka
Keyword(s):  
Image Classification ◽  
High Speed ◽  
Data Representation ◽  
Practical Significance ◽  
Classification Methods ◽  
Software Modeling ◽  
Space Efficiency ◽  
Software Models ◽  
Applied Software ◽  
Structural Image

The subject of this research is the image classification methods based on a set of key points descriptors. The goal is to increase the performance of classification methods, in particular, to improve the time characteristics of classification by introducing hashing tools for reference data representation. Methods used: ORB detector and descriptors, data hashing tools, search methods in data arrays, metrics-based apparatus for determining the relevance of vectors, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using hash structures, which speeds up the calculation dozens of times; the classification time for the considered experimental descriptions increases linearly with decreasing number of hashes; the minimum metric value limit choice on setting the class for object descriptors significantly affects the accuracy of classification; the choice of such limit can be optimized for fixed samples databases; the experimentally achieved accuracy of classification indicates the efficiency of the proposed method based on data hashing. The practical significance of the work is - the classification model’s synthesis in the hash data representations space, efficiency proof of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems.

scholarly journals RESEARCH OF EFFECTIVENESS OF STRUCTURAL IMAGE CLASSIFICATION METHODS USING CLUSTER DATA MODEL

2017 ◽  
Vol 0 (3) ◽  
pp. 78-85 ◽  
Author(s):  
V. A. Gorokhovatsky ◽  
Y. P. Putyatin ◽  
V. S. Stolyarov
Keyword(s):  
Image Classification ◽  
Data Model ◽  
Classification Methods ◽  
Cluster Data ◽  
Structural Image

A survey of image classification methods and techniques

2014 ◽  
Author(s):  
Siddhartha Sankar Nath ◽  
Girish Mishra ◽  
Jajnyaseni Kar ◽  
Sayan Chakraborty ◽  
Nilanjan Dey
Keyword(s):  
Image Classification ◽  
Classification Methods ◽  
Methods And Techniques

scholarly journals Effective combining of color and texture descriptors for indoor-outdoor image classification

2014 ◽  
Vol 27 (3) ◽  
pp. 399-410 ◽  
Author(s):  
Stevica Cvetkovic ◽  
Sasa Nikolic ◽  
Slobodan Ilic
Keyword(s):  
Image Classification ◽  
Accurate Method ◽  
Optimal Combination ◽  
Compact Representation ◽  
Svm Classifier ◽  
Classification Methods ◽  
Empirical Tests ◽  
Public Datasets ◽  
Fast Extraction ◽  
Classification Procedures

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors in terms of fast extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets.

scholarly journals Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

scholarly journals AIRBORNE HYPERSPECTRAL REMOTE SENSING FOR IDENTIFICATION GRASSLAND VEGETATION

2015 ◽  
Vol XL-3/W3 ◽  
pp. 427-431 ◽  
Author(s):  
P. Burai ◽  
T. Tomor ◽  
L. Bekő ◽  
B. Deák
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Training Sample ◽  
Hyperspectral Data ◽  
Training Dataset ◽  
Classification Methods ◽  
Grassland Vegetation ◽  
Training Samples ◽  
Almost All ◽  
Noise Fraction

In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

scholarly journals Comparison of Three Image Classification Methods in Urban Environment

2015 ◽  
Vol 03 (02) ◽  
pp. 54-59 ◽  
Author(s):  
Malgorzata Verőné Wojtaszek ◽  
Valéria Balázsik ◽  
Tamás Jancsó ◽  
Margit Horoszné Gulyás ◽  
Qingyan Meng
Keyword(s):  
Image Classification ◽  
Urban Environment ◽  
Classification Methods

scholarly journals 2507 A novel multi-photon microscopy method for neuronavigation in deep brain stimulation surgery

2018 ◽  
Vol 2 (S1) ◽  
pp. 2-3
Author(s):  
Nicholas M. George ◽  
Arianna G. Polese ◽  
Greg Futia ◽  
Baris Ozbay ◽  
Wendy Macklin ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Second Harmonic Generation ◽  
Image Classification ◽  
Blood Vessels ◽  
Harmonic Generation ◽  
Calcium Binding ◽  
Second Harmonic ◽  
Brain Regions ◽  
Classification Methods ◽  
Traditional Methods

OBJECTIVES/SPECIFIC AIMS: The goal for this project is to determine the feasibility of using a novel multi-photon fiber-coupled microscope to aid surgeons in localizing STN during surgeries. In order to accomplish this goal, we needed to identify the source of a strong autofluorescent signal in the STN and determine whether we could use image classification methods to automatically distinguish STN from surrounding brain regions. METHODS/STUDY POPULATION: We acquired 3 cadaveric brains from the University of Colorado Anschutz Medical Campus, Department of Pathology. Two of these brains were non-PD controls whereas 1 was diagnosed with PD. We dissected a 10 square centimeter region of midbrain surrounding STN, then prepared this tissue for slicing on a vibratome or cryostat. Samples were immuno-labeled for various cellular markers for identification, or left unlabeled in order to observe the autofluorescence for image classification. RESULTS/ANTICIPATED RESULTS: The border of STN is clearly visible based on the density of a strong autofluorescent signal. The autofluorescent signal is visible using 2-photon (850–1040 nm excitation) and conventional confocal microscopy (488–647 nm excitation). We were also able to visualize blood vessels with second harmonic generation. The autofluorescent signal is quenched by high concentrations of Sudan-black B (0.5%–5%), and is primarily localized in microtubule-associated protein-2 (MAP2)+ cells, indicating that it is likely lipofuscin accumulation in neurons. Smaller lipofuscin particles also accumulate in microglia, identified based on ionized calcium binding adopter 1 (Iba1)+ labeling. We anticipate that colocalization analysis will confirm these qualitative observations. Using 2-photon images of the endogenous autofluorescent signal in these samples, we trained a logistic regression-based image classifier using features derived from gray-level co-occurrence matrices. Preliminary testing indicates that our classifier performed well, with a mean accuracy of 0.89 (standard deviation of 0.11) and a Cohen’s Kappa value of 0.76 (standard deviation of 0.24). We are currently using coherent anti-Stokes Raman scattering and third harmonic imaging to identify different features of myelin that can be used to distinguish between these regions and expect similar results. DISCUSSION/SIGNIFICANCE OF IMPACT: Traditional methods for localizing STN during DBS surgery include the use of stereotactic coordinates and multi-electrode recording (MER) during implantation. MERs are incredibly useful in DBS surgeries, but require penetration of brain structures in order to infer location. Using multi-photon microscopy techniques to aid identification of STN during DBS surgeries offers a number of advantages over traditional methods. For example, blood vessels can be clearly identified with second harmonic generation, something that is not possible with MER. Multi-photon microscopy also allows visualization deep into tissue without actually penetrating it. This ability to look within a depth of field is useful for detection of STN borders based on autofluorescent cell density. When combined with traditional stereotactic information, our preliminary image classification methods are a fast, reliable way to provide surgeons with extra information concerning their location in the midbrain. We anticipate that future advancements and refinements to our image classifier will only increase accuracy and the potential applications and value. In summary, these preliminary data support the feasibility of multi-photon microscopy to aid in the identification of target brain regions during DBS surgeries. The techniques described here complement and enhance current stereotactic and electrophysiological methods for DBS surgeries.

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