scholarly journals Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4024 ◽  
Author(s):  
David Santos ◽  
Eric Lopez-Lopez ◽  
Xosé M. Pardo ◽  
Roberto Iglesias ◽  
Senén Barro ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Minimum Spanning Tree ◽  
Scene Recognition ◽  
Public Image ◽  
Support Vector ◽  
Automatic Identification ◽  
Correct Identification ◽  
Generalization Capability ◽  
Key Points ◽  
Visual Descriptors

Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center.

scholarly journals Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Anitha Ganesan ◽  
Anbarasu Balasubramanian
Keyword(s):  
State Of The Art ◽  
Characteristic Curve ◽  
Scene Recognition ◽  
Color Histogram ◽  
Support Vector ◽  
Svm Classifier ◽  
Scene Classification ◽  
Visual Descriptors ◽  
Outdoor Scenes ◽  
Institute Of Technology

AbstractIn the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)—95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs—2.08% and 4.92%, respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors

2019 ◽  
Vol 73 (1) ◽  
pp. 37-55 ◽  
Author(s):  
B. Anbarasu ◽  
G. Anitha
Keyword(s):  
State Of The Art ◽  
Scene Recognition ◽  
Support Vector ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Speeded Up Robust Features ◽  
Indoor Scene ◽  
Visual Descriptors ◽  
Pyramid Matching ◽  
Spatial Pyramid

In this paper, a new scene recognition visual descriptor called Enhanced Scale Invariant Feature Transform-based Sparse coding Spatial Pyramid Matching (Enhanced SIFT-ScSPM) descriptor is proposed by combining a Bag of Words (BOW)-based visual descriptor (SIFT-ScSPM) and Gist-based descriptors (Enhanced Gist-Enhanced multichannel Gist (Enhanced mGist)). Indoor scene classification is carried out by multi-class linear and non-linear Support Vector Machine (SVM) classifiers. Feature extraction methodology and critical review of several visual descriptors used for indoor scene recognition in terms of experimental perspectives have been discussed in this paper. An empirical study is conducted on the Massachusetts Institute of Technology (MIT) 67 indoor scene classification data set and assessed the classification accuracy of state-of-the-art visual descriptors and the proposed Enhanced mGist, Speeded Up Robust Features-Spatial Pyramid Matching (SURF-SPM) and Enhanced SIFT-ScSPM visual descriptors. Experimental results show that the proposed Enhanced SIFT-ScSPM visual descriptor performs better with higher classification rate, precision, recall and area under the Receiver Operating Characteristic (ROC) curve values with respect to the state-of-the-art and the proposed Enhanced mGist and SURF-SPM visual descriptors.

scholarly journals Evaluating Grayware Characteristics and Risks

2011 ◽  
Vol 2011 ◽  
pp. 1-28 ◽  
Author(s):  
Zhongqiang Chen ◽  
Zhanyan Liang ◽  
Yuan Zhang ◽  
Zhongrong Chen
Keyword(s):  
Information Gain ◽  
Feature Space ◽  
Training Data ◽  
Support Vector ◽  
Learning Models ◽  
Generalization Capability ◽  
Self Organizing Maps ◽  
Defense Strategies ◽  
Security Applications ◽  
Vector Machines

Grayware encyclopedias collect known species to provide information for incident analysis, however, the lack of categorization and generalization capability renders them ineffective in the development of defense strategies against clustered strains. A grayware categorization framework is therefore proposed here to not only classify grayware according to diverse taxonomic features but also facilitate evaluations on grayware risk to cyberspace. Armed with Support Vector Machines, the framework builds learning models based on training data extracted automatically from grayware encyclopedias and visualizes categorization results with Self-Organizing Maps. The features used in learning models are selected with information gain and the high dimensionality of feature space is reduced by word stemming and stopword removal process. The grayware categorizations on diversified features reveal that grayware typically attempts to improve its penetration rate by resorting to multiple installation mechanisms and reduced code footprints. The framework also shows that grayware evades detection by attacking victims' security applications and resists being removed by enhancing its clotting capability with infected hosts. Our analysis further points out that species in categoriesSpywareandAdwarecontinue to dominate the grayware landscape and impose extremely critical threats to the Internet ecosystem.

A Novel Approach of Eye Detection Based on Haar-Like Feature and SVM

2011 ◽  
Vol 121-126 ◽  
pp. 1863-1867
Author(s):  
Yu Hang Guo ◽  
Jie Liu
Keyword(s):  
Support Vector ◽  
Expression Recognition ◽  
Generalization Capability ◽  
Eye Detection ◽  
Novel Approach ◽  
Detection Technology ◽  
Training Samples ◽  
Face Detection And Recognition ◽  
Fatigue Detection ◽  
Background Image

Recognizing the importance of eye detection technology in face detection and recognition, facial expression recognition, driver fatigue detection, and so on, the current paper presents a novel approach to eye detection in images based on AdaBoost and the Support Vector Machine (SVM). The proposed method can accurately detect eyes with different postures, facial expressions, skin colors, and glasses in a simple background image. First, a Haar-like feature AdaBoost classifier based on the developed training samples is used to detect eyes in images. An SVM post classifier trained by coordinates of the true and pseudo eye rectangles is then used to optimize the results. Experimental results show that the proposed method is fast, robust, and has high generalization capability.

Fuel Identification Based on the Least Squares Support Vector Machines

2011 ◽  
Vol 317-319 ◽  
pp. 1237-1240 ◽  
Author(s):  
Yao Song Huang ◽  
Shi Liu ◽  
Jie Li ◽  
Lei Jia ◽  
Zhi Hong Li
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Power Plants ◽  
Detection System ◽  
Support Vector ◽  
Correct Identification ◽  
Identification Rate ◽  
Vector Machines ◽  
Flame Detection ◽  
Correct Identification Rate

The identification of the fuel types plays an important role in ensuring the safety and economics of the power plants. In order to obtain the flame signal in the process of combustion, a flame detection system is designed and a laboratorial platform is constructed. This paper extracts the signal parameters—the mean, the peak-peak value, the flicker frequency, and the flicker intensity —and takes them as the characteristic quantities of the flame signal. Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed. The result of the identification is more ideal, with the correct identification rate up to 100%. This shows that the method combined the four characteristic quantities with the LSSVM can obtain a good result in the identification of the fuel types.

Tifinagh Characters Recognition Using Simple Geometric Shapes

2016 ◽  
Vol 3 (1) ◽  
pp. 235
Author(s):  
M. Boutaounte ◽  
Y. Ouadid
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Present Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Branch Points ◽  
End Points ◽  
Geometric Shapes ◽  
New Methods ◽  
Key Points

<p>In this paper we present method of features extraction based on decomposition of the characters into several simple geometric shapes (segment, arc) by detecting the branch points and end points, as it explained follow a new methods are using to treated the obtained information in order to decide if the characters need to add more key points also in this step we extracte the type of the shapes (segment or arc) and the orientation. The next step of characters recognition different methods are used such as neural network (NN), K-mean and support vector machine (SVM) classifier. The results shown in this paper are obtained using the IRCAM database.</p>

Quantitative Estimation with Lift-Off Effect in Conductive Structure

2013 ◽  
Vol 712-715 ◽  
pp. 2035-2038
Author(s):  
Hua Yi Tang ◽  
Shu Li Pan ◽  
Ping Jie Huang ◽  
Di Bo Hou ◽  
Guang Xin Zhang
Keyword(s):  
Surface Defect ◽  
Experimental Validation ◽  
Quantitative Estimation ◽  
Support Vector ◽  
Testing Technique ◽  
Swarm Optimization ◽  
Model Free ◽  
Actual Use ◽  
Lift Off ◽  
Novel Model

Eddy current testing technique has been widely used in a variety of fields, many researches have been done in quantitative estimation in conductive structure. In the actual use of ECT system, lift-off effect is an inevitable factor which is still a challenging task. Hence, the objectives of this study are to introduce a novel model-free method Support Vector Regression optimized by Particle Swarm Optimization (PSO-SVR) to estimate the surface defect with variable lift-off. Experimental validation carried out that the proposed method had a good performance in surface defect estimation with lift-off effect.

scholarly journals Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm

2016 ◽  
Vol 10 (7) ◽  
pp. 29 ◽  
Author(s):  
Jaber Soltani ◽  
Moosa Kalanaki ◽  
Mohammad Soltani
Keyword(s):  
Support Vector Regression ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Support Vector ◽  
Failure Rates ◽  
Input Output ◽  
Generalization Capability ◽  
Comparative Tests

This paper proposes a Support Vector Regression (SVR) based on Fuzzified Input-output Variables which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to predict data from training ones. Then, results from proposed Fuzzified SVR-PSO (FSVR-PSO) model are compared with other methods; comparative tests are performed using pipe failures data. The analysis and the experimental results show this method has high comprehensibility as well as satisfactory generalization capability.

A Novel Classification Method of Fish Based on Multi-Feature Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 1513-1519 ◽  
Author(s):  
Wei Dong Du ◽  
Bao Wei Chen ◽  
Hai Sen Li ◽  
Chao Xu
Keyword(s):  
Discrete Cosine Transform ◽  
Feature Fusion ◽  
Acoustic Scattering ◽  
Scattering Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Identification ◽  
Classification Problems ◽  
Cosine Transform ◽  
Identification Rate

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

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