An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain

Zhang, Jing; Lei, Sui; Li, Zhenwei; Yue, Yang

doi:10.1016/j.neucom.2012.11.029

Cited by 36 publications

(14 citation statements)

References 25 publications

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“…1, and to perform extraction of SIFT descriptor from low-resolution images, see our previous work [21].…”

Section: Social Images Tag Ranking Based On Visual Words In Compressementioning

confidence: 99%

“…Obviously, tag-ranking in pixel domain will be time-consuming. An effective method is to process image data in compressed domain, which can make full use of image compression processing and characteristics of the compressed data [21]. Suresh [22] presented a technique to index an image using DC and AC coefficients to extract features from chrominance and luminance planes separately.…”

Section: Introductionmentioning

confidence: 99%

“…The method utilized the spatial relationship of DCT coefficients between a block and its sub-blocks. In our previous work, much research about compressed domain has been explored [20][21][24][25][26]. Based on these studies, considering the compressed social images, social images tag ranking based on visual words in compressed domain is proposed in order to reduce the tag ranking time under ensuring the accuracy of social image tags.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Social images tag ranking based on visual words in compressed domain

Zhang

Liu

et al. 2015

Neurocomputing

Self Cite

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“…1, and to perform extraction of SIFT descriptor from low-resolution images, see our previous work [21].…”

Section: Social Images Tag Ranking Based On Visual Words In Compressementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Social images tag ranking based on visual words in compressed domain

Zhang

Liu

et al. 2015

Neurocomputing

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“…There are 50 images for each animal. And each image is represented by the bagof-words model [21], [22] using SIFT as local features [23], [24].…”

Section: ) Female Pop Star Face Datasetmentioning

confidence: 99%

Nonnegative Matrix Factorization using Class Label Information

Kokoye

Oke

Izogie

2013

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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Nonnegative matrix factorization (NMF) has been a powerful tool for finding out parts-based, linear representations of nonnegative data samples. Nevertheless, NMF is an unsupervised algorithm, and it is not able to utilize the class label information. In this paper, the Nonnegative Matrix Factorization using Class Label Information (NMF-CLI) is proposed. It combines the class label information for factorization constraints. The proposed NMF-CLI method is investigated with one cost function and the corresponding update rules are given. Experiment results show the power of the proposed novel algorithm, by comparing to the state-of-the-art methods.

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“…Reference [7] proposed a visual word extraction method based on compressed domain. Reference [8] introduced the model of visual attention in compressed domain to build the visual words.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning for Compressed Pornographic Image Recognition

Wang

Zhang

Liu

2015

2015 IEEE International Conference on Multimedia Big Data

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A compressed pornographic image recognition method is proposed by using incremental learning. For describing pornographic image, visual words are created from lowresolution (LR) image reconstructed from the compressed stream of the pornographic image. Covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic image. At last, incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples. The experimental results show that the proposed incremental learning method for compressed pornographic image has higher recognition rate as well as costs less recognition time.

show abstract

An approach of bag-of-words based on visual attention model for pornographic images recognition in compressed domain

Cited by 36 publications

References 25 publications

Social images tag ranking based on visual words in compressed domain

Social images tag ranking based on visual words in compressed domain

Nonnegative Matrix Factorization using Class Label Information

Incremental Learning for Compressed Pornographic Image Recognition

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