The Image Classification with Different Types of Image Features

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

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ON GEOMETRIC AND ORTHOGONAL MOMENTS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001400000581 ◽

2000 ◽

Vol 14 (07) ◽

pp. 875-894 ◽

Cited By ~ 50

Author(s):

JUN SHEN ◽

WEI SHEN ◽

DANFEI SHEN

Keyword(s):

Pattern Analysis ◽

Equivalence Theorem ◽

Image Features ◽

Shape Classification ◽

Orthogonal Moments ◽

Zero Crossings ◽

Different Types ◽

Base Functions ◽

Different Shapes ◽

Function Discontinuity

Moments are widely used in pattern recognition, image processing, computer vision and multiresolution analysis. To clarify and to guide the use of different types of moments, we present in this paper a study on the different moments and compare their behavior. After an introduction to geometric, Legendre, Hermite and Gaussian–Hermite moments and their calculation, we analyze at first their behavior in spatial domain. Our analysis shows orthogonal moment base functions of different orders having different number of zero-crossings and very different shapes, therefore they can better separate image features based on different modes, which is very interesting for pattern analysis and shape classification. Moreover, Gaussian–Hermite moment base functions are much more smoothed, they are thus less sensitive to noise and avoid the artifacts introduced by window function discontinuity. We then analyze the spectral behavior of moments in frequency domain. Theoretical and numerical analyses show that orthogonal Legendre and Gaussian–Hermite moments of different orders separate different frequency bands more effectively. It is also shown that Gaussian–Hermite moments present an approach to construct orthogonal features from the results of wavelet analysis. The orthogonality equivalence theorem is also presented. Our analysis is confirmed by numerical results, which are then reported.

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Modified Bag of Visual Words Model for Image Classification

Al-Nahrain Journal of Science ◽

10.22401/anjs.24.2.11 ◽

2021 ◽

Vol 24 (2) ◽

pp. 78-86

Author(s):

Zainab N. Sultani ◽

◽

Ban N. Dhannoon ◽

Keyword(s):

Image Classification ◽

Classification Performance ◽

Image Features ◽

Local Feature ◽

Bag Of Visual Words ◽

Scale Invariant ◽

Visual Words ◽

Challenging Tasks ◽

Feature Information ◽

Scale Invariant Feature

Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.

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A Method of Objects Classification Based on Learning and Visual Perception

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20183620359 ◽

2018 ◽

Vol 36 (2) ◽

pp. 359-367

Author(s):

Na Li ◽

Xinbo Zhao ◽

Yongjia Yang ◽

Xiaochun Zou

Keyword(s):

Eye Tracking ◽

Visual Saliency ◽

Image Features ◽

Classification Method ◽

Attention Model ◽

Whole Process ◽

Visual Behaviors ◽

Different Types ◽

Visual Saliency Model ◽

High Level

Objects classification is one of the most significant problems in computer vision. For improving the accuracy of objects classification, we put forward a new classification method enlightened the whole process that human distinguish different types of objects. Our method mixed visual saliency model and CNN, is more close to human and has apparently biological advantages. Firstly, we built an eye-tracking database to learn people visual behaviors when they classify various objects and recorded the eye-tracking data. Secondly, this database is used to train a learning-based visual attention model, which is based on low-level (e.g., orientation, color, intensity, etc.) and high-level (e.g., faces, people, cars, etc.) image features to analyze and predict the human's classification RoIs. Finally, we established a CNN framework to classify RoIs. The results of the experiment showed our attention model can determine saliency regions and predict human's classification RoIs more precisely and our classification method improved the efficiency of classification markedly.

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A Hybrid Classification Method of Medical Image Based on Deep Learning

10.21203/rs.3.rs-836474/v1 ◽

2021 ◽

Author(s):

Yulong Wang ◽

Xiaofeng Liao ◽

Dewen Qiao ◽

Jiahui Wu

Keyword(s):

Deep Learning ◽

Image Classification ◽

Medical Image ◽

Rapid Development ◽

Medical Science ◽

Image Features ◽

Image Feature ◽

Classification Method ◽

Support Vector ◽

Medical Image Classification

Abstract With the rapid development of modern medical science and technology, medical image classification has become a more and more challenging problem. However, in most traditional classification methods, image feature extraction is difficult, and the accuracy of classifier needs to be improved. Therefore, this paper proposes a high-accuracy medical image classification method based on deep learning, which is called hybrid CQ-SVM. Specifically, we combine the advantages of convolutional neural network (CNN) and support vector machine (SVM), and integrate the novel hybrid model. In our scheme, quantum-behaved particle swarm optimization algorithm (QPSO) is adopted to set its parameters automatically for solving the SVM parameter setting problem, CNN works as a trainable feature extractor and SVM optimized by QPSO performs as a trainable classifier. This method can automatically extract features from original medical images and generate predictions. The experimental results show that this method can extract better medical image features, and achieve higher classification accuracy.

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Image classification of vaginal microecology detection based on gabor texture and LSTM model

Technology and Health Care ◽

10.3233/thc-213509 ◽

2021 ◽

pp. 1-18

Author(s):

Gaoteng Yuan ◽

Yinping Dong ◽

Xiaofeng Zhou

Keyword(s):

Image Classification ◽

Texture Analysis ◽

Short Term Memory ◽

Imbalanced Data ◽

Region Of Interest ◽

Texture Features ◽

Image Features ◽

Imbalanced Data Sets ◽

Area Of Interest ◽

Gynecological Diseases

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

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