Feature Extraction Technique using Discrete Wavelet Transform for Image Classification

Ghazali, Kamarul Hawari; Mansor, Mohd Fais; Mustafa, Mohd. Marzuki; Hussain, Aini

doi:10.1109/scored.2007.4451366

Cited by 85 publications

(37 citation statements)

References 5 publications

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“…On critical observation of the results given in the Tables, it has been found that wavelet features are well suited for the purpose stated above and ANN results are promising in comparison to the methods of the previous research works in this regard. The work by Ghazali et al (2007) made use of DWT features and demonstrated an accuracy of 87.25% in classifying broad and narrow weeds. Comparatively, our investigation showed an improved accuracy of 92.65% with the use of DWT features.…”

Section: Discussionmentioning

confidence: 99%

“…Cucumbers were classified as per the European Grading Standards by Clement et al (2013) with 99% accuracy. Narrow and broad weed were classified based on DWT features by Ghazali et al (2007) with an accuracy of 87.25%. Palm oil fresh fruit bunches were automatically graded by Jamil et al (2009) with the help of neuro fuzzy systems to an extent of 73.3%.…”

Section: ____________________________________________________________mentioning

confidence: 99%

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Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Kumar¹,

Rajpurohit²,

Jirage³

2018

Journal of Horticultural Research

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Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems because of human operators. In the present paper, an efficient machine vision system is designed and implemented in order to assess the quality of pomegranate fruits. The main objectives of the present study are (1) to adopt a best pre-processing module, (2) to select best class of features and (3) to develop an efficient machine learning technique for quality assessment of pomegranates. The sample images of pomegranate fruits are captured using a custom-made image acquisition system. Two sets of features, namely, spatial domain feature set and wavelet feature set are extracted for all of the sample images. Experiments are conducted by training both artificial neural networks (ANNs) and support vector machines (SVMs) using both sets of features. The results of the experiments illustrated that ANNs outperform SVMs with a difference in the accuracy of 12.65%. Further, the selection of wavelet featureset for training yielded more accurate results against spatial domain feature set.Key words: Pomegranate quality assessment, wavelet features for fruit quality analysis, machine intelligence in pomegranate quality assessment, ANN for fruit quality assessment

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Section: Discussionmentioning

confidence: 99%

Section: ____________________________________________________________mentioning

confidence: 99%

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Kumar¹,

Rajpurohit²,

Jirage³

2018

Journal of Horticultural Research

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“…This group of pixels is represented as texture elements [4,18].The aim of the weed discrimination analysis was to classify the objects derived by image segmentation into defined number of classes according to their specific features. The wavelet energy texture features were efficiently used for texture classification and segmentation.…”

Section: Feature Extractionmentioning

confidence: 99%

A Study on Weed Discrimination Through Wavelet Transform, Texture Feature Extraction and Classification

D¹,

Prema²

2015

IJCSIT

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“…Our work is based on the HSV color histogram feature extraction. Second, we will consider texture feature as Discrete Wavelet Transform (DWT) [9]. Texture refers to visual patterns with properties of homogeneity that do not result from the presence of only a single color such as clouds and water.…”

Section: Global Image Featurementioning

confidence: 99%

Automatic Classification for Various Images Collections Using Two Stages Clustering Method

Cho¹,

Na²,

Choi³

et al. 2013

OJAppS

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In this paper, we propose an automatic classification for various images collections using two stage clustering method. Here, we have used global and local image features. First, we review about various types of feature vector that is suitable to represent local and global properties of images, and similarity measures that can be represented an affinity between these images. Second, we consider a clustering method for image collection. Here, we first build a coarser clustering by partitioning various images into several clusters using the flexible Mean shift algorithm and K-mean clustering algorithm. Second, we construct dense clustering of images collection by optimizing a Gaussian Dirichlet process mixture model taking initial clusters as given coarser clustering. Finally, we have conducted the comparative experiments between our method and existing methods on various images datasets. Our approach has significant advantage over existing techniques. Besides integrating temporal and image content information, our approach can cluster automatically photographs without some assumption about number of clusters or requiring a priori information about initial clusters and it can also generalize better to different image collections.

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Feature Extraction Technique using Discrete Wavelet Transform for Image Classification

Cited by 85 publications

References 5 publications

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features

A Study on Weed Discrimination Through Wavelet Transform, Texture Feature Extraction and Classification

Automatic Classification for Various Images Collections Using Two Stages Clustering Method

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