Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework

Aarzoo Patwa; Ashish Kumar Khare; Vinod Patel

International Journal of Image Processing and Pattern Recognition

Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework

Abstract

Salient object identification has received a lot of attention recently in computer vision because it offers quick solutions to several challenging procedures. It first identifies the scene's most prominent and attention-grabbing object, after which it segments the object's entire extent. The perception and processing of visual input is linked to visual saliency. This proposed work focuses on several convectional saliency detection methods that are useful in a variety of applications such as image segmentation, image compression, object identification, image classification, and image retrieval. Here we presented a new Canny Edge Based Contrast Object Saliency detection algorithm, Automatic segmentation, and enhancement of a large number of important objects in the image without expensive training materials. The proposed strategy is calculated on several image dimensions at a level of conspicuity criteria. Our approach beats other recent schemes in terms of precision and recall, Fmeasure, and accuracy, while staying simple, fast, and smart, according to experimental results.

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How to cite

Aarzoo Patwa, Ashish Kumar Khare, Vinod Patel. Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework. International Journal of Image Processing and Pattern Recognition. 2023;9(1):8–21.

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Using Image Feature Selection through Deep Salient Region Detection Using Canny Edge Detection Framework

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