Abstract
Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods.
Highlights
Image segmentation is the task of dividing the image into different regions, each one of which ideally belongs to the same object or content
We present an image thresholding method based on the criteria of global quantum entropy maximization (GQEM), which has an equivalent object function to Otsu’s, but gives more explicit physical interpretation of image thresholding in the language of quantum mechanics
We will start with a new method, which utilizes the criteria of global quantum entropy maximization to achieve optimal thresholding, and propose a novel encoding scheme
Summary
Image segmentation is the task of dividing the image into different regions, each one of which ideally belongs to the same object or content. In the last two decades, a wide variety of segmentation techniques have been developed, which conventionally fall into the following two categories [2]: layer-based and block-based segmentation methods [10,11] Among all these techniques, the thresholding methods offer numerous advantages such as smaller storage space, fast processing and ease in manipulation. Parametric approaches assume that the intensity distributions of images obey the Gaussian mixture (GM) model, which means the number and parameters of Gaussians in the mixture (the model selection) must be determined [13] These problems have been traditionally solved by considering the expectation maximization (EM) algorithm [14] or gradient-based methods [15,16], the methods are time consuming.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
