AN EFFICIENT KEYPOINT FEATURE EXTRACTION TECHNIQUES TO DETECT COPY MOVE IMAGE FORGERY

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

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Comparison between different feature extraction techniques for audio-visual speech recognition

Journal on Multimodal User Interfaces ◽

10.1007/bf02884428 ◽

2007 ◽

Vol 1 (1) ◽

pp. 7-20 ◽

Cited By ~ 13

Author(s):

Alin G. Chiţu ◽

Leon J. M. Rothkrantz ◽

Pascal Wiggers ◽

Jacek C. Wojdel

Keyword(s):

Feature Extraction ◽

Speech Recognition ◽

Visual Speech ◽

Extraction Techniques ◽

Visual Speech Recognition

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Classification using Extended Morphological Attribute Profiles based on different feature extraction techniques

2011 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2011.6050221 ◽

2011 ◽

Cited By ~ 8

Author(s):

Stijn Peeters ◽

Prashanth R Marpu ◽

Jon A Benediktsson ◽

Mauro Dalla Mura

Keyword(s):

Feature Extraction ◽

Extraction Techniques ◽

Morphological Attribute

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ECG Beats Classification Using Mixture of Features

International Scholarly Research Notices ◽

10.1155/2014/178436 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 21

Author(s):

Manab Kumar Das ◽

Samit Ari

Keyword(s):

Feature Extraction ◽

Extraction Methods ◽

Ectopic Beat ◽

Extraction Techniques ◽

Efficient System ◽

Ecg Signals ◽

Temporal Features ◽

S Transform ◽

Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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Correlative consideration concerning feature extraction techniques for speech recognition — A review

2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) ◽

10.1109/iccpct.2016.7530308 ◽

2016 ◽

Author(s):

Arshpreet Kaur ◽

Amitoj Singh ◽

Virender Kadyan

Keyword(s):

Feature Extraction ◽

Speech Recognition ◽

Extraction Techniques

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Decision fusion-based approach for content-based image classification

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2016-0025 ◽

2017 ◽

Vol 10 (3) ◽

pp. 310-331 ◽

Cited By ~ 4

Author(s):

Sudeep Thepade ◽

Rik Das ◽

Saurav Ghosh

Keyword(s):

Feature Extraction ◽

Image Classification ◽

Image Recognition ◽

Extraction Techniques ◽

Data Set ◽

Content Type ◽

Knowledge Fusion ◽

Knn Classifier ◽

Query Classification ◽

First Time

Purpose Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques. Design/methodology/approach Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. Findings The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose. Originality/value To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

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