Salient regions detection using background superpixels

Foolad, Shima; Maleki, Ali

doi:10.1109/iraniancee.2016.7585729

Cited by 2 publications

(1 citation statement)

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“…To solve the above problems, Harel et al. [22] proposed a Graph‐Based Visual Saliency (GBVS)‐based detection method. This method retained the visual feature extraction method of Itti model but used the Markov transfer matrix to measure the saliency map.…”

Section: Related Workmentioning

confidence: 99%

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Chen

Tong

et al. 2022

CAAI Trans on Intel Tech

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Unconstrained face images are interfered by many factors such as illumination, posture, expression, occlusion, age, accessories and so on, resulting in the randomness of the noise pollution implied in the original samples. In order to improve the sample quality, a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary. First, the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary. Then, a block cooperation framework is presented to perform sparse coding for different local structures of human face, and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients. Finally, by synthesising the sparse representation results of all visual salient block dictionaries, the global coding residual is obtained and the class label is given. The experimental results on four databases, that is, AR, extended Yale B, LFW and PubFig, indicate that the combination of visual saliency dictionary, block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

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Section: Related Workmentioning

confidence: 99%