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Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features

arXiv.org Artificial Intelligence

Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.


Deep Learning for Computer Vision with TensorFlow 2

#artificialintelligence

You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.


"Not hotdog" vs. mission-critical AI applications for the enterprise

#artificialintelligence

Check out "Designing Data-Intensive Applications" to explore the pros and cons of various technologies for processing and storing data, and to learn how to make full use of data in modern applications. Artificial intelligence has come a long way since the concept was introduced in the 1950s. Until recently, the technology had an aura of intrigue, and many believed its place was strictly inside research labs and science fiction novels. Today, however, the technology has become very approachable. The popular TV show Silicon Valley recently featured an app called "Not Hotdog," based on cutting-edge machine learning frameworks, showcasing how easy it is to create a deep learning application.


Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications

arXiv.org Machine Learning

JPEG2000 (j2k) is a highly popular format for image and video compression.With the rapidly growing applications of cloud based image classification, most existing j2k-compatible schemes would stream compressed color images from the source before reconstruction at the processing center as inputs to deep CNNs. We propose to remove the computationally costly reconstruction step by training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients directly extracted from j2k-compressed images. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy in the DWT domain. Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost. This way, faster and more accurate classification is possible for j2k encoded images without image reconstruction. Through experiments on CIFAR-10 and Tiny ImageNet data sets, we show that the performance of the proposed solution is consistent for image transmission over limited channel bandwidth.


To believe or not to believe: Validating explanation fidelity for dynamic malware analysis

arXiv.org Artificial Intelligence

Converting malware into images followed by vision-based deep learning algorithms has shown superior threat detection efficacy compared with classical machine learning algorithms. When malware are visualized as images, visual-based interpretation schemes can also be applied to extract insights of why individual samples are classified as malicious. In this work, via two case studies of dynamic malware classification, we extend the local interpretable model-agnostic explanation algorithm to explain image-based dynamic malware classification and examine its interpretation fidelity. For both case studies, we first train deep learning models via transfer learning on malware images, demonstrate high classification effectiveness, apply an explanation method on the images, and correlate the results back to the samples to validate whether the algorithmic insights are consistent with security domain expertise. In our first case study, the interpretation framework identifies indirect calls that uniquely characterize the underlying exploit behavior of a malware family. In our second case study, the interpretation framework extracts insightful information such as cryptography-related APIs when applied on images created from API existence, but generate ambiguous interpretation on images created from API sequences and frequencies. Our findings indicate that current image-based interpretation techniques are promising for explaining vision-based malware classification. We continue to develop image-based interpretation schemes specifically for security applications.