Spam Email Image Classification Based on Text and Image Features

2019 ◽  
Author(s):  
Estqlal Hammad Dhah ◽  
Mohammed Abdullah Naser ◽  
Suhad A. Ali
Keyword(s):  
Image Classification ◽  
Image Features ◽  
Text And Image

scholarly journals Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

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.

scholarly journals Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1502
Author(s):  
Ben Wilkes ◽  
Igor Vatolkin ◽  
Heinrich Müller
Keyword(s):  
Visual Analysis ◽  
Binary Classification ◽  
Statistical Significance ◽  
Image Features ◽  
Support Vector ◽  
Audio Signals ◽  
Text And Image ◽  
The Impact ◽  
Music Track

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

Text-Image Retrieval With Salient Features

2021 ◽  
Vol 32 (4) ◽  
pp. 1-13
Author(s):  
Xia Feng ◽  
Zhiyi Hu ◽  
Caihua Liu ◽  
W. H. Ip ◽  
Huiying Chen
Keyword(s):  
Image Retrieval ◽  
Recall Rate ◽  
Image Features ◽  
Feature Representation ◽  
Image Feature ◽  
Text And Image ◽  
Retrieval Task ◽  
Retrieval Method ◽  
Salient Features ◽  
Object Level

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

scholarly journals Modified Bag of Visual Words Model for Image Classification

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.

scholarly journals A Hybrid Classification Method of Medical Image Based on Deep Learning

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.

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

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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