Colour and texture feature‐based image retrieval by using Hadamard matrix in discrete wavelet transform

Farsi, Hassan; Mohamadzadeh, Sajad

doi:10.1049/iet-ipr.2012.0203

Cited by 39 publications

(28 citation statements)

References 19 publications

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“…Farsi and Mohamadzadeh (2013) proposed a method using the combination of precision and recall criteria as performance measures for the CBIR and CBVR systems. The precision and recall criteria are given by Eq.…”

Section: Results Evaluation Measuresmentioning

confidence: 99%

“…In this paper, we used the Hadamard matrix and Discrete Wavelet Transform (HDWT) method to achieve the CBVR (Farsi and Mohamadzadeh, 2013). The Intensity plane of the proposed method provides an acceptable performance and the size of the feature vector is satisfactory.…”

Section: Intensity-hdwt Methodsmentioning

confidence: 99%

“…The size of the feature vector of the HueMaximum-Minimum-Difference (HMMD) color space is three times bigger than the Intensity, therefore, we use the Intensity plane instead of the HMMD planes because the size of the feature vector plays an important role in the proposed method. The HDWT method has been briefly explained as below (Farsi and Mohamadzadeh, 2013). …”

Section: Intensity-hdwt Methodsmentioning

confidence: 99%

“…In previous steps, we have proposed and modified new algorithms for the shot boundary detection and the key frame extraction. In the following, we propose and compare the retrieval methods via sparse representation and Intensity-HDWT (Farsi and Mohamadzadeh, 2013).…”

Section: Retrieval Algorithmmentioning

confidence: 99%

“…According to Farsi and Mohamadzadeh (2013), P(1) has been adopted, with precision at 100% recall (i.e., precision after retrieving all of the relevant documents). P(1) is number of relevant images divided by the total number of images that are retrieved.…”

Section: Number Of Relevant Images Retrieved Precisionmentioning

confidence: 99%

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Content Based Video Retrieval Based on HDWT and Sparse Representation

Mohamadzadeh

Farsi

2016

Image Anal Stereol

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Video retrieval has recently attracted a lot of research attention due to the exponential growth of video datasets and the internet. Content based video retrieval (CBVR) systems are very useful for a wide range of applications with several type of data such as visual, audio and metadata. In this paper, we are only using the visual information from the video. Shot boundary detection, key frame extraction, and video retrieval are three important parts of CBVR systems. In this paper, we have modified and proposed new methods for the three important parts of our CBVR system. Meanwhile, the local and global color, texture, and motion features of the video are extracted as features of key frames. To evaluate the applicability of the proposed technique against various methods, the P(1) metric and the CC_WEB_VIDEO dataset are used. The experimental results show that the proposed method provides better performance and less processing time compared to the other methods.Keywords: content based video retrieval (CBVR), Hadamard matrix and discrete wavelet transform (HDWT), key frame extraction, shot boundary detection, sparse representation.

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Section: Results Evaluation Measuresmentioning

confidence: 99%

Section: Intensity-hdwt Methodsmentioning

confidence: 99%

Section: Intensity-hdwt Methodsmentioning

confidence: 99%

Section: Retrieval Algorithmmentioning

confidence: 99%

Section: Number Of Relevant Images Retrieved Precisionmentioning

confidence: 99%

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