Feature Selection For Image Classification Research Articles

Effect of Genetic Algorithm as a Feature Selection for Image Classification

Analysis of image content is important in the classification of images, identification, retrieval, and recognition processes. The medical image datasets for content-based medical image retrieval ( are large datasets that are limited by high computational costs and poor performance. The aim of the proposed method is to enhance this image retrieval and classification by using a genetic algorithm (GA) to choose the reduced features and dimensionality. This process was created in three stages. In the first stage, two algorithms are applied to extract the important features; the first algorithm is the Contrast Enhancement method and the second is a Discrete Cosine Transform algorithm. In the next stage, we used datasets of the medical images using GA-based feature selection to find feature vectors. Images from the datasets and images from the query are recognized using a correlation coefficient. The third stage of the proposed method used a diverse density algorithm feedback technique to enhance the performance of the . Images of breast cancer, brain cancer, lung cancer, thyroid cancer, etc., may be retrieved using the suggested procedure. By using a feature selection algorithm based on GA to determine the best subset of features, the challenge of system dimensionality is reduced. The suggested method has greater accuracy in precision, recall, and F-score than the other techniques.

Feature selection for image classification using ESSO

Feature selection is one of the major components of the data processing flow that correctly selects real-time entities for categorization. It is utilized in numerous research fields, including the machine learning approach. Feature selection and classification are beneficial to the processing of biomedical data in high-importance, high-dimensional datasets. Due to numerous obstacles in research, the current implementations are inadequate for predicting classification accuracy. To handle the classification challenge, we require dedicated neural network and classification model approaches. Initial features are selected using a method called "fuzzy c-means clustering with rough set theory" and are subsequently classified using "support vector machines." The learning machines' accuracy is seriously harmed by irrelevant and redundant features. The curse of dimensionality makes it difficult to locate data clusters in high-dimensional space. Data in the irrelevant dimensions may cause a lot of noise when the dimensionality grows. In addition, the current approach has a significant flaw: it is extremely time-consuming. The proposed solution used efficient feature subset selection in high-dimensional data to fix these problems. To begin with, our recommended method used Enhanced Social Spider Optimization (ESSO) computation, in which the standard Social Spider Optimization is improved with the aid of optimal radial based calculation to select the best highlights. When categorizing data, the Optimal Radial Basis Function Neural Network (ORBFNN) is used. Methods for calculating Artificial Bee Colony (ABC) are used to streamline RBFNN's sufficiency in characterizing smaller scale show information.

Image feature selection embedded distribution differences between classes for convolutional neural network

Convolutional neural networks have achieved a great success in feature extraction and classification of images. However, some of the features extracted by convolutional neural networks are with insignificant difference between classes, which not only contribute little to image classification, but also increase the complexity of the classifier. It is important to select features that are helpful for image classification when using convolutional neural network. In view of the existence of class labels of image samples when training classifier, and motivated by the intention that these labels may also play a certain role in feature selection for image classification, we propose a feature selection approach by taking the distribution differences between classes into consideration on the basis of the features extracted by convolutional neural network. To be specific, we use the Gaussian mixture model to approximate the distribution of each feature on each subclass, and select the features significantly contribute to classification by designing a measure of distribution difference according to the numerical characteristics described by Gaussian mixture models. Further, an image classifier can be presented by redesigning the fully connected layers of the convolutional neural network based on the selected features. The proposed feature selection is adopted to image classification, and the experimental results show the effectiveness of the method.

Unsupervised feature selection for image classification: A bipartite matching-based principal component analysis approach

In this paper, we propose a general unsupervised feature selection method named unsupervised feature selection using principal component analysis (UFSPCA). Repetitive information causes redundancy in information and if there is a correlation between features, it is not easy to understand the information is repetitive. Accordingly, we first use PCA to create uncorrelated and orthogonal features, then calculate the similarities between the original and uncorrelated features. Next, we modeled two sets of original and orthogonal features and their similarity between them to a weighted bipartite graph. Finally, we obtained a matching with the maximum weight using the Hungarian algorithm. The vertices of the original features that are in this matching are the selected features. To illustrate the optimality and efficiency of the proposed method, we evaluated the performance of our proposed method on five datasets using the KNN classifier and compared it with seven well-known unsupervised feature selection algorithms. The evaluation results show that the UFSPCA method is superior to the other seven algorithms.

Low-rank sparse feature selection for image classification

There is a lot of redundancy in the high dimensional raw images, which not only greatly increases the computational burden of image classification process, but also inevitably degrades the classification performance of the model. High-performance dimensionality reduction algorithms are in urgent need of development. To solve this problem, we develop a novel feature selection model for dimension reducing. It greatly reduces redundant features and selects the most representative features for classification. Besides, we also design a novelty version of the lightweight convolutional neural network (newCNN). The newCNN can enhance the classification performance of the system. To improve the classification accuracy, we build a hybrid classification (HC) model with the newCNN and Support Vector Machines (SVM). This model not only solves the problem of overfitting in the training process, but also has excellent generalization ability and robustness. The experimental results verify the effectiveness of our proposed methods.

Eye tracking data guided feature selection for image classification

Feature selection has played a critical role in image classification, since it is able to remove irrelevant and redundant features and to eventually reduce the dimensionality of feature space. Although existing feature selection methods have achieved promising progress, human factors have seldom been taken into account. To tackle such a problem, a novel two-stage feature selection method is proposed for image classification by taking human factors into account and leveraging the value of eye tracking data. In the coarse selection stage, with the help of eye tracking data, Regions of Interests (ROIs) from the human perspective are first identified to represent an image with visual features. Then, with an improved quantum genetic algorithm (IQGA) that incorporates a novel mutation strategy for alleviating the premature convergence, a subset of features is obtained for the subsequent fine selection. In the fine selection stage, a hybrid method is proposed to integrate the efficiency of the minimal-Redundancy-Maximal-Relevance (mRMR) and the effectiveness of the Support Vector Machine based Recursive Feature Elimination (SVM-RFE). In particular, the ranking criterion of the SVM-RFE is improved by incorporating the ranking information obtained from the mRMR. Comprehensive experimental results in two benchmark datasets demonstrate that eye tracking data are of great importance to improve the performance of feature selection for image classification.

Feature Selection for Image Classification Based on a New Ranking Criterion

In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief- SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.

Wavelet Feature Selection for Image Classification

Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.