UC-Merced Image Classification with CNN Feature Reduction Using Wavelet Entropy Optimized with Genetic Algorithm

Abstract

The classification of high-resolution and remote sensed terrain images with high accuracy is one of the greatest challenges in machine learning. In the present study, a novel CNN feature reduction using Wavelet Entropy Optimized with Genetic Algorithm (GA-WEE-CNN) method was used for remote sensing images classification. The optimal wavelet family and optimal value of the parameters of the Wavelet Sure Entropy (WSE), Wavelet Norm Entropy (WNE), and Wavelet Threshold Entropy (WTE) were calculated, and given to classifiers such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The efficiency of the proposed hybrid method was tested using the UC-Merced dataset. 80% of the data were used as training data, and a performance rate of 98.8% was achieved with SVM classifier, which has been the highest ratio compared to all studies using same dataset so far with only 18 features. These results proved the advantage of the proposed method.

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

“…A CNN architecture includes Convolutional layers (CONV), Activation layers (ACT) Pool layers (POOL), Fullyconnected layers (FC), and Classification layers (CLASS). Some of these layers are used again in order to increase CNN performances [15,16]. The present study benefited from AlexNet, VGG-19, SqueezeNet, and ResNet-50, which are among the most widely used CNN architectures, for feature extraction.…”

Color Correction and Repair of Haze Images Under Hue-saturation-intensity Color Space and Machine Learning

“…Based on formula (9), formula (10), formula (11) and formula (1), the image degradation model that can obtain the HSI color space is:…”

“…Therefore, an accurate estimation of the transmittance and atmospheric light of the haze image is the key to improving the dehazing effect. The dark primary color a priori algorithm is to obtain the transmittance and atmospheric light of the image by comparing the original image and the haze image, which has strong limitations.In order to solve this problem, a machine learning algorithm is proposed to capture the features of haze images and estimate the transmittance of haze images more accurately.This article first uses the HIS color space method to construct the haze image-transmittance graph library [8]; Secondly, the machine learning algorithm is constructed using the k-means clustering algorithm optimized by the density parameter method and the GA-SVM algorithm of the support vector machine improved by the genetic algorithm [9][10][11]. The machine learning algorithm is used to train the haze transmittance image library to obtain visual words with different characteristics, and use these visual words with different characteristics to construct a visual dictionary.…”

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

“…As ELMo [11], BERT [12] and other models have been proposed, text representation not only considers the morphological information of the word but also takes into account the context and semantic information. Recently, in the field of artificial intelligence and law, various neural network architectures such as CNN [13] and RNN [14] have been used for document embedding. Jiang et al [15] use deep reinforcement learning methods to improve classification accuracy.…”

“…For a given case, the task of LJP aims to empower machines to predict the judgment results (e.g., law articles, charges, and prison terms) of the case. Inspired by the success of deep learning techniques [13,14,21] on NLP tasks, researchers attempt to employ neural models to handle judgment prediction tasks. Some popular neural network methods are used in an automatic charge prediction task [22][23][24], and there are some works focusing on identifying applicable law articles for a given case [25][26][27].…”