Object detection by color histogram-based fuzzy classifier with support vector learning

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.

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Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Sensors ◽

10.3390/s20216019 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6019

Author(s):

José Manuel Lozano Domínguez ◽

Faroq Al-Tam ◽

Tomás de J. Mateo Sanguino ◽

Noélia Correia

Keyword(s):

Machine Learning ◽

Smart Cities ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Models ◽

Fuzzy Classifier ◽

Logistic Regression Models ◽

The Road ◽

Learning Agent ◽

Machine Learning Models

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

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

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-019-0030-9 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 2

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.

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Fuzzy classifier with support vector learning for image retrieval using a specified object

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/icsmc.2012.6378156 ◽

2012 ◽

Author(s):

Guo-Cyuan Chen ◽

Chia-Feng Juang

Keyword(s):

Image Retrieval ◽

Support Vector ◽

Fuzzy Classifier

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An Object Detection Method Based on Independent Local Features

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2006.p0744 ◽

2006 ◽

Vol 18 (6) ◽

pp. 744-750

Author(s):

Ryouta Nakano ◽

◽

Kazuhiro Hotta ◽

Haruhisa Takahashi

Keyword(s):

Object Detection ◽

Detection Method ◽

Principal Component ◽

Component Analysis ◽

Local Features ◽

Local Feature ◽

Superior Performance ◽

Support Vector ◽

Car Detection ◽

Training Samples

This paper presents an object detection method using independent local feature extractor. Since objects are composed of a combination of characteristic parts, a good object detector could be developed if local parts specialized for a detection target are derived automatically from training samples. To do this, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. We then used the basis vectors derived by ICA as independent local feature extractors specialized for a detection target. These feature extractors are applied to a candidate area, and their outputs are used in classification. However, the number of dimension of extracted independent local features is very high. To reduce the extracted independent local features efficiently, we use Higher-order Local AutoCorrelation (HLAC) features to extract the information that relates neighboring features. This may be more effective for object detection than simple independent local features. To classify detection targets and non-targets, we use a Support Vector Machine (SVM). The proposed method is applied to a car detection problem. Superior performance is obtained by comparison with Principal Component Analysis (PCA).

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