Image Classification and Retrieval with Kernel Methods

2011 ◽  
pp. 325-345
Author(s):  
Francesca Odone ◽  
Alessandro Verri
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Classification ◽  
Kernel Methods ◽  
Image Features ◽  
Kernel Functions ◽  
Current Work ◽  
Classification Problems ◽  
Image Representations ◽  
Open Issues

In this chapter we review some kernel methods useful for image classification and retrieval applications. Starting from the problem of constructing appropriate image representations, we describe in depth and comment on the main properties of various kernel engineering approaches that have been recently proposed in the computer vision and machine learning literature for solving a number of image classification problems. We distinguish between kernel functions applied to images as a whole and kernel functions looking at image features. We conclude by presenting some current work and discussing open issues.

scholarly journals Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 936
Author(s):  
Jianli Shao ◽  
Xin Liu ◽  
Wenqing He
Keyword(s):  
Machine Learning ◽  
Spatial Association ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Classification Problems ◽  
Rare Class ◽  
Data Adaptive

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning

2021 ◽  
pp. 242-251
Author(s):  
Waishan Qiu ◽  
Wenjing Li ◽  
Xun Liu ◽  
Xiaokai Huang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Urban Design ◽  
Design Theory ◽  
Large Scale ◽  
Image Features ◽  
Subjective Measures ◽  
Visual Elements ◽  
Urban Amenities ◽  
Human Perceptions

AbstractRecently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.

scholarly journals Image Classification Using Reinforcement Learning

2020 ◽  
Vol 23 (6) ◽  
pp. 1172-1191
Author(s):  
Artem Aleksandrovich Elizarov ◽  
Evgenii Viktorovich Razinkov
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Reinforcement Learning ◽  
Image Classification ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Further Development

Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence. The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.

scholarly journals Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2953 ◽  
Author(s):  
Jessica Fernandes Lopes ◽  
Leniza Ludwig ◽  
Douglas Fernandes Barbin ◽  
Maria Victória Eiras Grossmann ◽  
Sylvio Barbon
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Human Consumption ◽  
Barley Flour ◽  
Quality Aspects ◽  
Spatial Pyramid

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

scholarly journals Analysis of heterogeneous genomic samples using image normalization and machine learning

BMC Genomics ◽  
2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Sunitha Basodi ◽  
Pelin Icer Baykal ◽  
Alex Zelikovsky ◽  
Pavel Skums ◽  
Yi Pan
Keyword(s):  
Machine Learning ◽  
Molecular Epidemiology ◽  
Image Classification ◽  
Sequence Data ◽  
Image Data ◽  
Genomic Data ◽  
Next Generation Sequencing Data ◽  
Classification Problems ◽  
Image Normalization ◽  
Heterogeneous Populations

Abstract Background Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datasets of different sizes and structures. Results We propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important problems in molecular epidemiology: inference of viral infection stage and detection of viral transmission clusters using next-generation sequencing data. The infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy. Conclusions Sequence image normalization method allows for a robust conversion of genomic data into numerical data and overcomes several issues associated with employing machine learning methods to viral populations. Image data also help in the visualization of genomic data. Experimental results demonstrate that the proposed method can be successfully applied to different problems in molecular epidemiology and surveillance of viral diseases. Simple binary classifiers and clustering techniques applied to the image data are equally or more accurate than other models.

scholarly journals A Novel Luminance-Based Algorithm for Classification of Semi-Dark Images

2021 ◽  
Vol 11 (18) ◽  
pp. 8694
Author(s):  
Mehak Maqbool Memon ◽  
Manzoor Ahmed Hashmani ◽  
Aisha Zahid Junejo ◽  
Syed Sajjad Rizvi ◽  
Adnan Ashraf Arain
Keyword(s):  
Computer Vision ◽  
Image Classification ◽  
State Of The Art ◽  
Image Features ◽  
Small Error ◽  
Visual Scene ◽  
Color Model ◽  
Training Time ◽  
Single Pixel

Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.

scholarly journals PERANCANGAN APLIKASI MOBILE UNTUK KLASIFIKASI SAYURAN MENGGUNAKAN DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 300-306
Author(s):  
Muhamad Jaelani Akbar ◽  
Mochamad Wisuda Sardjono ◽  
Margi Cahyanti ◽  
Ericks Rachmat Swedia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Test Accuracy ◽  
Final Test ◽  
Testing Data

Sayuran merupakan sebutan bagi bahan pangan asal tumbuhan yang biasanya mengandung kadar air tinggi dan dikonsumsi dalam keadaan segar atau setelah diolah secara minimal. Keanekaragaman sayur yang terdapat di dunia menyebabkan keragaman pula dalam pengklasifikasian sayur. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali jenis sayuran dengan cepat dan mudah. Dalam penelitian ini jumlah jenis sayuran yang digunakan sebanyak 7 jenis diantara: brokoli, jagung, kacang panjang, pare, terung ungu, tomat dan kubis. Dataset yang digunakan berjumlah 941 gambar sayur dari 7 jenis sayur, ditambah 131 gambar sayur dari jenis yang tidak terdapat pada dataset, selain itu digunakan 291 gambar selain sayuran. Untuk melakukan klasifikasi jenis sayuran digunakan algoritme Convolutional Neural Network (CNN), yang merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan pesat. CNN merupakan salah satu algoritme yang terdapat pada metode Deep Learning dengan memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek citra. Uji coba dilakukan pada lima perangkat selular berbasiskan sistem operasi Android. Python digunakan sebagai bahasa pemrograman dalam merancang aplikasi mobile ini dengan menggunakan modul Tensor flow untuk melakukan training dan testing data. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Hasil final test accuracy yang diperoleh yaitu didapat keakuratan mengenali jenis sayuran sebesar 98.1% dengan salah satu hasil pengujian yaitu klasifikasi sayur jagung dengan akurasi sebesar 99.98049%.

scholarly journals Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

Drones ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 5
Author(s):  
Hafiz Suliman Munawar ◽  
Fahim Ullah ◽  
Amirhossein Heravi ◽  
Muhammad Jamaluddin Thaheem ◽  
Ahsen Maqsoom
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Crack Detection ◽  
Conditional Random Fields ◽  
Image Features ◽  
Superior Performance ◽  
Training Procedure ◽  
Generative Adversarial Network ◽  
Hybrid Approaches ◽  
Deep Crack

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.

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