The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumptio
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The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumption is not always correct in practice, and in some cases, salient regions merely cause visual distractions. Therefore, in different applications, a mechanism is needed to identify these regions. To prevent from distracting observer’s attention from the main subject, these regions are eliminated. Furthermore, neglecting these regions could be of considerable assistance to the methods that function base on salient regions recognition. So, in this paper, Based on the methods of the class imbalance challenge each segment of training images in the dataset is a partition to 9 classes according to the relevant mask in the dataset, that the number of each class is proportional to its disturbance intensity. Then, segment-based features are extracted and determining the class of each segment is determined according to WTPTSSR method, which is based on the Sparse Coding and Representation system.Finally, in order to precisely analyzing the proposed method and comparing it to other approaches, four analysis criteria with different performances are presented. According to results, despite being time-consuming, the proposed method has a higher accuracy than the previous ones.
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