Effective Visual Saliency Detection Method Using Reduced Color and Texture Features

Subject Areas : Renewable energy

Masoud khazaee Fadafen ¹ , Naser Mehrshad ² , Seyyed Mohammad Razavi ³

1 - Department of Electrical Engineering- Technical and Vocational University, Tehran, Iran
2 - Department of Electrical and Computer Engineering- Birjand University, Birjand, Iran
3 - Department of Electrical and Computer Engineering- Birjand University, Birjand, Iran

Received: 2021-11-28 Accepted : 2022-03-16 Published : 2023-08-23

Keywords: Feature extraction, Human visual system, Saliency Map, computational model of simple cell,

Abstract :

In this study, an effective and efficient algorithm for detection a saliency map is presented based on the modeling of the rapid response of the human visual system to changes in the intensity, texture and color. Some cases such as inspiration from performance of human visual system, requiring no training, reduce number of image colors, reduce color channels and Proper use of the least texture information in this algorithm have increased its efficiency. In the proposed method in the first step , Due to sensitivity of the human visual system to higher contrast signals, only higher contrast channel has been used to extract the color saliency map, Then the intensity saliency map as well as the texture saliency map are extracted using the intensity component in lab color space using Simple cell computational model of the visual cortex and finally, with the perfect combination of the saliency maps of the color, the intensity, and the texture, object saliency map is obtained. The proposed method and existing methods have been tested on MSRA10K and ECSSD databases. The results of the implementations show that the proposed hybrid algorithm for the detection saliency map using the dominant color and texture features, On the ECSSD database, the mean absolute error, F-measure score and the area under the ROC curve are 0.173, 0.789 and 0.891, respectively, and on the MSRA10K database are 0.178, 0.790 and 0.919, respectively, compared to other models, it indicates better performance of the proposed method than other methods.

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