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

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.

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

Journal of Navigation ◽

10.1017/s0373463319000420 ◽

2019 ◽

Vol 73 (1) ◽

pp. 37-55 ◽

Cited By ~ 1

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.

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Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Sensors ◽

10.3390/s21134283 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4283

Author(s):

Md.-Habibur Rahman ◽

Md. Shahjalal ◽

Moh. Khalid Hasan ◽

Md.-Osman Ali ◽

Yeong-Min Jang

Keyword(s):

Light Emitting Diode ◽

Characteristic Curve ◽

Analysis Data ◽

Image Sensor ◽

Data Rate ◽

Support Vector ◽

Svm Classifier ◽

High Data ◽

Receiver System ◽

Optical Camera Communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

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Scene Recognition Based on Recurrent Memorized Attention Network

Electronics ◽

10.3390/electronics9122038 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2038

Author(s):

Xi Shao ◽

Xuan Zhang ◽

Guijin Tang ◽

Bingkun Bao

Keyword(s):

Recognition Accuracy ◽

State Of The Art ◽

Classification Performance ◽

Scene Recognition ◽

Experimental Results ◽

Scene Classification ◽

Attention Network ◽

Attention Model ◽

Scene Image ◽

Object Based

We propose a new end-to-end scene recognition framework, called a Recurrent Memorized Attention Network (RMAN) model, which performs object-based scene classification by recurrently locating and memorizing objects in the image. Based on the proposed framework, we introduce a multi-task mechanism that contiguously attends on the different essential objects in a scene image and recurrently performs memory fusion of the features of object focused by an attention model to improve the scene recognition accuracy. The experimental results show that the RMAN model has achieved better classification performance on the constructed dataset and two public scene datasets, surpassing state-of-the-art image scene recognition approaches.

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Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

Remote Sensing ◽

10.3390/rs11161943 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1943 ◽

Cited By ~ 15

Author(s):

Omid Rahmati ◽

Saleh Yousefi ◽

Zahra Kalantari ◽

Evelyn Uuemaa ◽

Teimur Teimurian ◽

...

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Characteristic Curve ◽

Machine Learning Techniques ◽

Support Vector ◽

Mountainous Area ◽

Data Set ◽

Boosted Regression Tree ◽

Hazard Exposure ◽

Learning Techniques

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

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

Sensors ◽

10.3390/s19184024 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4024 ◽

Cited By ~ 1

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.

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ELECTROCARDIOGRAM BEAT CLASSIFICATION USING S-TRANSFORM BASED FEATURE SET

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519414500663 ◽

2014 ◽

Vol 14 (05) ◽

pp. 1450066 ◽

Cited By ~ 6

Author(s):

MANAB KUMAR DAS ◽

SAMIT ARI

Keyword(s):

Feature Extraction ◽

Support Vector ◽

Svm Classifier ◽

Massachusetts Institute Of Technology ◽

Ecg Signals ◽

Beat Classification ◽

Temporal Features ◽

S Transform ◽

Institute Of Technology ◽

Stockwell Transform

In this paper, the conventional Stockwell transform is effectively used to classify the ECG arrhythmias. The performance of ECG classification mainly depends on feature extraction based on an efficient formation of morphological and temporal features and the design of the classifier. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not selected properly. Here, the S-transform (ST) is used to extract the morphological features which is appended with temporal features. This feature set is independently classified using artificial neural network (NN) and support vector machine (SVM). In this work, five classes of ECG beats (normal, ventricular, supra ventricular, fusion and unknown beats) from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database are classified according to AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). Performance is evaluated on several normal and abnormal ECG signals of MIT-BIH arrhythmias database using two classifier techniques: ST with NN classifier (ST-NN) and other proposed ST with SVM classifier (ST-SVM). The proposed method achieves accuracy of 98.47%. The performance of the proposed technique is compared with ST-NN and earlier reported technique.

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Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

Computational Intelligence and Neuroscience ◽

10.1155/2015/909123 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

Loris Nanni ◽

Sheryl Brahnam ◽

Stefano Ghidoni ◽

Alessandra Lumini

Keyword(s):

State Of The Art ◽

Parameter Tuning ◽

General Purpose ◽

Extensive Study ◽

Support Vector ◽

Svm Classifier ◽

Random Subspace ◽

Sum Rule ◽

Multiple Datasets ◽

Heterogeneous Ensemble

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset.

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Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2021010103 ◽

2021 ◽

Vol 16 (1) ◽

pp. 56-74

Author(s):

Weiwei Yang ◽

Haifeng Song

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

State Of The Art ◽

Support Vector ◽

Svm Classifier ◽

Spatial Classification ◽

Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

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Algorithmic-Level Approximate Tensorial SVM Using High-Level Synthesis on FPGA

Electronics ◽

10.3390/electronics10020205 ◽

2021 ◽

Vol 10 (2) ◽

pp. 205

Author(s):

Hamoud Younes ◽

Ali Ibrahim ◽

Mostafa Rizk ◽

Maurizio Valle

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Machine Learning Algorithms ◽

High Level Synthesis ◽

Support Vector ◽

Svm Classifier ◽

Approximate Computing ◽

Classification Framework ◽

Time Latency ◽

High Level

Approximate Computing Techniques (ACT) are promising solutions towards the achievement of reduced energy, time latency and hardware size for embedded implementations of machine learning algorithms. In this paper, we present the first FPGA implementation of an approximate tensorial Support Vector Machine (SVM) classifier with algorithmic level ACTs using High-Level Synthesis (HLS). A touch modality classification framework was adopted to validate the effectiveness of the proposed implementation. When compared to exact implementation presented in the state-of-the-art, the proposed implementation achieves a reduction in power consumption by up to 49% with a speedup of 3.2×. Moreover, the hardware resources are reduced by 40% while consuming 82% less energy in classifying an input touch with an accuracy loss less than 5%.

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Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Sensors ◽

10.3390/s21144802 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4802

Author(s):

Roger Resmini ◽

Lincoln Silva ◽

Adriel S. Araujo ◽

Petrucio Medeiros ◽

Débora Muchaluat-Saade ◽

...

Keyword(s):

Breast Cancer ◽

Early Diagnosis ◽

Operating Characteristic ◽

Cancer Survival ◽

Characteristic Curve ◽

Breast Cancer Diagnosis ◽

Support Vector ◽

Svm Classifier ◽

Diagnose Breast Cancer ◽

Causes Of Mortality

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

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