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 Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty

2016 ◽  
Vol 6 (6) ◽  
pp. 1235-1240
Author(s):  
H. Alizadeh ◽  
B. Minaei Bidgoli
Keyword(s):  
Customer Loyalty ◽  
Accurate Method ◽  
Support Vector ◽  
Similar Data ◽  
Classification Methods ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Step Model ◽  
Customer Classification ◽  
Loyal Customers

The main aim of this study was introducing a comprehensive model of bank customers᾽ loyalty evaluation based on the assessment and comparison of different clustering methods᾽ performance. This study also pursues the following specific objectives: a) using different clustering methods and comparing them for customer classification, b) finding the effective variables in determining the customer loyalty, and c) using different collective classification methods to increase the modeling accuracy and comparing the results with the basic methods. Since loyal customers generate more profit, this study aims at introducing a two-step model for classification of customers and their loyalty. For this purpose, various methods of clustering such as K-medoids, X-means and K-means were used, the last of which outperformed the other two through comparing with Davis-Bouldin index. Customers were clustered by using K-means and members of these four clusters were analyzed and labeled. Then, a predictive model was run based on demographic variables of customers using various classification methods such as DT (Decision Tree), ANN (Artificial Neural Networks), NB (Naive Bayes), KNN (K-Nearest Neighbors) and SVM (Support Vector Machine), as well as their bagging and boosting to predict the class of loyal customers. The results showed that the bagging-ANN was the most accurate method in predicting loyal customers. This two-stage model can be used in banks and financial institutions with similar data to identify the type of future customers.

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

Moving Object Classification in a Video Sequence

2017 ◽  
pp. 70-101
Author(s):  
S. Vasavi ◽  
T. Naga Jyothi ◽  
V. Srinivasa Rao
Keyword(s):  
Wavelet Transforms ◽  
Moving Objects ◽  
Performance Test ◽  
Texture Features ◽  
Classification Performance ◽  
Object Identification ◽  
Svm Classifier ◽  
Classification Methods ◽  
Military Installations ◽  
Outdoor Environments

Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

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