An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Avcı, Engin

doi:10.1016/j.eswa.2006.01.025

Cited by 70 publications

(25 citation statements)

References 29 publications

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“…Discrete Wavelet Transform (DWT) (Daubechies, 1990) has been proven to be very efficient in signal analysis of many engineering applications (Avci, 2007a(Avci, , 2007bUyar, Yildirim, & Gencoglu, 2008;Wu, Hsu, & Wu, 2009;Übeyli, 2009a;Xian & Zeng;2009). The main advantage of DWT is that it constructs a varying support (window size), being dilated (wide) for low frequency and sharp (narrow) for high frequency.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

Vong

Wong

2011

Expert Systems with Applications

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

Vong

Wong

2011

Expert Systems with Applications

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“…A set of frames is obtained which are sequentially matching which helps in the retrieval of videos. This method is simple but the computational cost depends upon the features size and is very high [3] . To strike a balance between the efficiency and computational cost, more visual features are used from the frames to represent a shot [2].…”

Section: Content Based Video Retrieval Systemsmentioning

confidence: 99%

“…Classification of key-frames indicates classification of corresponding shots. For a video retrieval, a shot is searched by identifying its key-frame [3], [4]. Computational cost involved while using all frames of a shot to retrieve a video is much higher than that when only key frames are used to represent a shot.…”

Section: Classification Of Videosmentioning

confidence: 99%

CBVR and Classification of Video Database–Latest Trends, Methods, Effective Techniques, Problems and Challenges

Ansari¹,

Vasishtha²

2015

IJCA

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Content Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos exist. Results have shown performance improvement (higher precision and recall values) when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. In this paper a complex and wide area of CBVR and CBVR systems has been presented in a comprehensive and simple way. Processes at different stages in CBVR systems are described in a systematic way. Types of features, their combinations and their utilization methods, techniques and algorithms are also shown. Various querying methods, some of the features like GLCM, Gabor Magnitude, algorithm to obtain similarity like Kullback-Leibler distance method and Relevance Feedback Method are discussed. Functioning of Support Vector Machines (SVM) is discussed which are vital for automatic classification of videos.

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“…An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature Zhang and Benveniste (1992); Marar (1997); Oussar and Dreyfus (2000); Chen and Hewit (2000); Zhang and San (2004); Fan and Wang (2005); Zhang and Pu (2006); Chen et al (2006); Avci (2007); Jiang et al (2007); Misra et al (2007).…”

Section: Introductionmentioning

confidence: 99%

Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

Marar¹,

Bordin²

2016

DAT

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Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.

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An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Cited by 70 publications

References 29 publications

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

CBVR and Classification of Video Database–Latest Trends, Methods, Effective Techniques, Problems and Challenges

Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

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