Abstract
Aiming at the problems of intensive background noise, low accuracy, and high computational complexity of the current significant object detection methods, the visual saliency detection algorithm based on Hierarchical Principal Component Analysis (HPCA) has been proposed in the paper. Firstly, the original RGB image has been converted to a grayscale image, and the original grayscale image has been divided into eight layers by the bit surface stratification technique. Each image layer contains significant object information matching the layer image features. Secondly, taking the color structure of the original image as the reference image, the grayscale image is reassigned by the grayscale color conversion method, so that the layered image not only reflects the original structural features but also effectively preserves the color feature of the original image. Thirdly, the Principal Component Analysis (PCA) has been performed on the layered image to obtain the structural difference characteristics and color difference characteristics of each layer of the image in the principal component direction. Fourthly, two features are integrated to get the saliency map with high robustness and to further refine our results; the known priors have been incorporated on image organization, which can place the subject of the photograph near the center of the image. Finally, the entropy calculation has been used to determine the optimal image from the layered saliency map; the optimal map has the least background information and most prominently saliency objects than others. The object detection results of the proposed model are closer to the ground truth and take advantages of performance parameters including precision rate (PRE), recall rate (REC), and F-measure (FME). The HPCA model’s conclusion can obviously reduce the interference of redundant information and effectively separate the saliency object from the background. At the same time, it had more improved detection accuracy than others.
Highlights
The human’s visual attention mechanism had enabled humans to do real-time positioning in complex scene images corresponding to the position of important information, in order to determine the priority sequence of different objectives, which can effectively reduce the range of visual processing, greatly saving computing resources
Based on the above analysis, in order to weaken the impact efficiency of redundant information on the detection results and retention of machine learning, the paper has proposed the saliency object detection algorithm based on the Hierarchical Principal Component Analysis (PCA) model, using the layered PCA method which divides the image into multilayer images of a lack of background information in different degrees, so that in the process of extracting principal component information in reducing the amount of calculation and weakening the background information of interference to the detection process, it retains the efficiency of machine learning, to increase the robustness of the algorithm
The Hierarchical PCA model in saliency detection is tested on datasets of MRAS-1000, ASD-1000, and ECSSD-1000 and compared with several methods, such as ITTI (IT) [1], GBVS (GB) [2], Spectral Residual (SR) [3], LC [25], Hierarchical Saliency (HS) [23], BSCA [26], HDCT [27], and DCRR [28]
Summary
The human’s visual attention mechanism had enabled humans to do real-time positioning in complex scene images corresponding to the position of important information, in order to determine the priority sequence of different objectives, which can effectively reduce the range of visual processing, greatly saving computing resources. Based on the above analysis, in order to weaken the impact efficiency of redundant information on the detection results and retention of machine learning, the paper has proposed the saliency object detection algorithm based on the Hierarchical PCA model, using the layered PCA method which divides the image into multilayer images of a lack of background information in different degrees, so that in the process of extracting principal component information in reducing the amount of calculation and weakening the background information of interference to the detection process, it retains the efficiency of machine learning, to increase the robustness of the algorithm. Complexity and reduces the interference of background information on the detection process in the process of extracting the principal component information; and (3) to preserve the efficiency of machine learning and increase the robustness of the proposed algorithm.
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