Eye tracking data guided feature selection for image classification

Zhou, Xuan; Gao, Xin; Wang, Jiajun; Yu, Hui; Wang, Zhiyong; Chi, Zheru

doi:10.1016/j.patcog.2016.09.007

Cited by 38 publications

(12 citation statements)

References 37 publications

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“…Although these studies are able to obtain a good classification result, the human factor has been ignored. In view of this, Zhou et al [194] proposed an eye-guided tracking feature selection method for this field, which explores the mechanisms of the human eye for processing visual information based on mRMR and SVM. Their method takes a new look at image classification even though they do not consider dynamic images.…”

Section: ) Image Classification and Background Suppressionmentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions. INDEX TERMS Feature selection, big data, data mining, applications.

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Section: ) Image Classification and Background Suppressionmentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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“…In the field of ant colony optimization there are some works devoted to image FS [11,9], which were able to improve classification results, using less features than other methods and reducing the processing times. A novel FS method was proposed in [117] for general image classification. It considers human factors and leverages the value of eye tracking data to find a subset of relevant input attributes, which are subsequently refined by a hybrid method combining FS based on mutual information (mRMR) and on SVMs.…”

Section: Image Classificationmentioning

confidence: 99%

Feature selection in image analysis: a survey

2019

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Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which features extract from them, and feature selection can help in this scenario. The goal of this paper is to survey the most recent feature selection methods developed and/or applied to image analysis, covering the most popular fields such as image classification, image segmentation, etc. Finally, an experimental evaluation on several popular datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate comparative studies for the research community.

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“…Wrapper methods based on support vector machine (SVM) have been widely used in the machine learning field. 33,34 We used the SVM recursive feature elimination (SVM-RFE) [33][34][35][36][37]…”

Section: Features Extractionmentioning

confidence: 99%

Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy

Okuwobi

Fan

et al. 2018

J. Med. Imag.

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We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient ([Formula: see text]) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.

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Eye tracking data guided feature selection for image classification

Cited by 38 publications

References 37 publications

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Feature selection in image analysis: a survey

Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy

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