Histograms, Wavelets and Neural Networks Applied to Image Retrieval

Gonzalez, Alain C.; Sossa, Juan H.; Riverón, Edgardo Manuel Felipe; Pogrebnyak, Oleksiy

doi:10.1007/11925231_78

Cited by 4 publications

(2 citation statements)

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“…Besides we can also quickly calculate the wavelet transform. So wavelet transform becomes one of the most effective methods, which are used to extract image features to classify (recognize) objects [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Extracting Hand Pose Features Using Wavelet Transformsmentioning

confidence: 99%

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Context-aware hand poses classifying on images and video-sequences using a combination of wavelet transforms, PCA and neural networks

Phan¹,

Trang²

2017

EAI Endorsed Transactions on Context-aware Systems and Applicat

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In this paper we propose novel context-aware algorithms for hand poses classifying on images and video-sequences. The proposed hand poses classifying on images algorithm based on Viola-Jones method, wavelet transform, PCA and neural networks. On the first step, the Viola-Jones method is used to find the location of hand pose on images. Then, on the second step, the features of hand pose are extracted using combination of wavelet transform and PCA. Finally, on the last step, these extracted features are classified by multi-layer feed-forward neural networks. The proposed hand poses classifying on video-sequences algorithm based on the combination of CAMShift algorithm and proposed hand poses classifying on images algorithm. The experimental results show that the proposed algorithms effectively classify the hand pose in difference light contrast conditions and compete with state-of-the-art algorithms.

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Section: Extracting Hand Pose Features Using Wavelet Transformsmentioning

confidence: 99%

“…It enables to obtain the necessary information about the image and it is also can be very quickly calculated. The experimental results of image classification algorithms [5][6][7][8][9][10] showed that images, features of which extracted by using wavelet transform, were classified with 76-99.7% accuracy rate.…”

Section: Introductionmentioning

confidence: 99%