scholarly journals Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images

2007 ◽  
Vol 29 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Erik R. Urbach ◽  
Jos B.T.M. Roerdink ◽  
Michael H.F. Wilkinson
Keyword(s):  
Gray Scale ◽  
Scale Invariant

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

Logo Matching and Recognition Based on Context

2018 ◽  
pp. 164-176
Author(s):  
Tapan Kumar Das
Keyword(s):  
Real World ◽  
Scale Invariant Feature Transform ◽  
Gray Scale ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Key Points ◽  
Gray Scale Image ◽  
Feature Transform ◽  
Graphic Elements ◽  
Scale Invariant Feature

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

scholarly journals Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Alejandra Cruz-Bernal ◽  
Martha M. Flores-Barranco ◽  
Dora L. Almanza-Ojeda ◽  
Sergio Ledesma ◽  
Mario A. Ibarra-Manzano
Keyword(s):  
Digital Image ◽  
Visual Inspection ◽  
Experimental Results ◽  
Gray Scale ◽  
High Expectation ◽  
Weak Classifier ◽  
White Region ◽  
Gray Scale Image ◽  
Global Representation

In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

A visual pathway for shape-based invariant classification of gray scale images

2007 ◽  
Vol 14 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Konstantinos A. Raftopoulos ◽  
Nikolaos Papadakis ◽  
Klimis Ntalianis ◽  
Stefanos Kollias
Keyword(s):  
Visual Pathway ◽  
Gray Scale

Doppler and gray-scale sonographic classification of adnexal torsion

2009 ◽  
Vol 34 (2) ◽  
pp. 208-211 ◽  
Author(s):  
R. Auslender ◽  
O. Shen ◽  
Y. Kaufman ◽  
Y. Goldberg ◽  
M. Bardicef ◽  
...  
Keyword(s):  
Adnexal Torsion ◽  
Gray Scale ◽  
Sonographic Classification

A Study of Spore Image Classification Based on Feature Extraction

2014 ◽  
Vol 556-562 ◽  
pp. 4774-4778
Author(s):  
Lu Lu Yue ◽  
Ming Yang ◽  
Li Peng
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Fungal Spore ◽  
Scale Invariant ◽  
Identification Of Fungi ◽  
Scale Invariant Feature ◽  
Future Work ◽  
Microscopy Images ◽  
Tremendous Impact

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using K-means clustering algorithm, at finally we use LDA, KNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

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