scholarly journals Image Aesthetic Assessment Based on Latent Semantic Features

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 223
Author(s):  
Gang Yan ◽  
Rongjia Bi ◽  
Yingchun Guo ◽  
Weifeng Peng
Keyword(s):  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Evaluation Model ◽  
Subjective Evaluation ◽  
Texture Features ◽  
Svm Classifier ◽  
Subjective Perception ◽  
Semantic Features ◽  
Aesthetic Quality ◽  
Aesthetic Evaluation

Image aesthetic evaluation refers to the subjective aesthetic evaluation of images. Computational aesthetics has been widely concerned due to the limitations of subjective evaluation. Aiming at the problem that the existing evaluation methods of image aesthetic quality only extract the low-level features of images and they have a low correlation with human subjective perception, this paper proposes an aesthetic evaluation model based on latent semantic features. The aesthetic features of images are extracted by superpixel segmentation that is based on weighted density POI (Point of Interest), which includes semantic features, texture features, and color features. These features are mapped to feature words by LLC (Locality-constrained Linear Coding) and, furthermore, latent semantic features are extracted using the LDA (Latent Dirichlet Allocation). Finally, the SVM classifier is used to establish the classification prediction model of image aesthetics. The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%.

Lung nodule classification using combination of CNN, second and higher order texture features

2021 ◽  
pp. 1-9
Author(s):  
Amrita Naik ◽  
Damodar Reddy Edla
Keyword(s):  
Malignant Tumors ◽  
Early Stage ◽  
Lung Nodule ◽  
Texture Features ◽  
Svm Classifier ◽  
Softmax Classifier ◽  
Spatial Features ◽  
High Level ◽  
Learning Architectures ◽  
Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

Sound Quality of Aircraft Cabin for VIP and Business Jets

2018 ◽  
Author(s):  
Nurkan Turkdogru Gurun ◽  
Hemang N. Sheth
Keyword(s):  
Prediction Model ◽  
Paired Comparison ◽  
Semantic Differential ◽  
Sound Quality ◽  
Subjective Perception ◽  
Subjective Test ◽  
Aircraft Cabin ◽  
Listening Tests ◽  
Potential Customers

This paper aims to identify the attributes that describe aircraft interior noise, determine most important psychoacoustic models that characterize cabin sounds, and construct a prediction model that can be utilized for VIP and business jets to evaluate subjective perception. In the first part, paired comparison listening tests and free verbalization are conducted with expert subjects who experienced VIP and business aircraft flight. The study generated a list of adjective pairs that describe perception of cabin sounds to be used for semantic differential listening tests. Multi-dimensional scaling is performed on paired comparison data. Results showed that subjects’ decisions can be categorized in loudness and annoyance dimensions which are not necessarily linearly associated. The second part of the study is the development of a sound quality prediction model for aircraft cabin. Semantic differential tests are conducted with potential customers. Objective sound quality metrics are correlated to subjective test responses using principal components regression. This model is found to be most effective explaining pleasantness, comfort, and loudness perception. It is intended to be utilized to modify/redesign noise control treatments and sound signature of an aircraft. All listening tests were conducted inside an aircraft cabin simulator considering the influence of visual content.

scholarly journals An Evaluation Method of Mode Switching Quality for Double-Belt Continuously Variable Transmission

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lanchun Zhang ◽  
Zhongwei Zhu ◽  
Bin Huang ◽  
Tianbo Wang
Keyword(s):  
Evaluation Method ◽  
Evaluation Model ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Transmission Efficiency ◽  
Continuously Variable Transmission ◽  
Mode Switching ◽  
Evaluation Indexes ◽  
Variable Transmission ◽  
Driver’S Intention

In order to improve the transmission efficiency and carrying capacity of conventional single-belt continuously variable transmission (CVT), one new type of dual-belt CVT is proposed in this paper. Under the situation that this new dual-belt CVT should be switched between single- and dual-belt modes frequently according to driver’s intention and road conditions, so five objective evaluation indexes of mode switching quality for the dual-belt CVT are proposed, considering the aspects of vehicle power, comfort, and transmission durability comprehensively. Then, the objective evaluation model of mode switching quality is established by the BP neural network optimized by the genetic algorithm. It is found that the prediction results are consistent with the subjective evaluation. After analyzing the influence of the selected five evaluation indexes on the prediction results, it is obvious that these five evaluation indexes of mode switching quality for dual-belt CVT are reasonable.

Moving Object Classification in a Video Sequence

2017 ◽  
pp. 70-101
Author(s):  
S. Vasavi ◽  
T. Naga Jyothi ◽  
V. Srinivasa Rao
Keyword(s):  
Wavelet Transforms ◽  
Moving Objects ◽  
Performance Test ◽  
Texture Features ◽  
Classification Performance ◽  
Object Identification ◽  
Svm Classifier ◽  
Classification Methods ◽  
Military Installations ◽  
Outdoor Environments

Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

scholarly journals Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

2020 ◽  
Vol 9 (2) ◽  
pp. 109 ◽  
Author(s):  
Bo Cheng ◽  
Shiai Cui ◽  
Xiaoxiao Ma ◽  
Chenbin Liang
Keyword(s):  
High Resolution ◽  
Extraction Method ◽  
Texture Features ◽  
Decomposition Methods ◽  
Projection Matrix ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Building Area ◽  
Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

scholarly journals Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaofu Huang ◽  
Ming Chen ◽  
Peizhong Liu ◽  
Yongzhao Du
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Markov Random Fields ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
Texture Features ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Malignant Tumours ◽  
Data Set

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

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