A New Semantic Approach to Improve Webpage Segmentation

Ghaemmaghami, Saeedeh Sadat Sajjadi; Miller, James

doi:10.13052/jwe1540-9589.2042

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…It is that the task of pixel by pixel classification. Semantic segmentation has a wide range of application scenarios in daily life, such as automatic driving, medical diagnosis and augmented reality [1,2]. Image semantic segmentation refers to the technology of recognising and segmenting each object in an image at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

A multilevel segmentation method of asymmetric semantics based on deep learning

Liu,

Yang

2023

IET Cyber-Phy Sys Theory & Ap

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An asymmetric semantic multi‐level segmentation method based on depth learning is proposed in order to improve the precision and effect of semantic segmentation. A ‘content tree’ structure and an adjacency matrix are constructed to represent the parent‐child relationship between each image sub region in a complete image. Through multiple combinations of spatial attention mechanism and channel attention mechanism, the similarity semantic features of the target object can be selectively aggregated, so as to enhance its feature expression and avoid the impact of significant objects. The asymmetric semantic segmentation model asymmetric pyramid feature convolutional network (APFCN) is constructed, and the path feature extraction and parameter adjustment are realised through APFCN. On the basis of APFCN network, a full convolution network is introduced for end‐to‐end image semantic segmentation. Combining the advantages of convolution network in extracting image features and the advantages of short‐term and short‐term memory network in solving long‐term dependence, an end‐to‐end hybrid depth network is constructed for image semantic multi‐level segmentation. The experimental results show that the mean intersection over Union value and mean pixel accuracy value are higher than that of the literature method, both of which are increased by more than 3%, and the segmentation effect is good.

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Section: Introductionmentioning

confidence: 99%

A multilevel segmentation method of asymmetric semantics based on deep learning

Liu,

Yang

2023

IET Cyber-Phy Sys Theory & Ap

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“…Fusion-Block [28] is Gestalt law-inspired and subsequential re-segmentation, which uses semantic text similarity.…”

mentioning

confidence: 99%

“…Integrated-Block [28] uses DOM structure, is vision-based, and uses text-based similarity metrics analysis based on web page segmentation.…”

mentioning

confidence: 99%

Web Page Content Block Identification with Extended Block Properties

Griazev

Ramanauskaitė

2023

Applied Sciences

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Web page segmentation is one of the most influential factors for the automated integration of web page content with other systems. Existing solutions are focused on segmentation but do not provide a more detailed description of the segment including its range (minimum and maximum HTML code bounds, covering the segment content) and variants (the same segments with different content). Therefore the paper proposes a novel solution designed to find all web page content blocks and detail them for further usage. It applies text similarity and document object model (DOM) tree analysis methods to indicate the maximum and minimum ranges of each identified HTML block. In addition, it indicates its relation to other blocks, including hierarchical as well as sibling blocks. The evaluation of the method reveals its ability to identify more content blocks in comparison to human labeling (in manual labeling only 24% of blocks were labeled). By using the proposed method, manual labeling effort could be reduced by at least 70%. Better performance was observed in comparison to other analyzed web page segmentation methods, and better recall was achieved due to focus on processing every block present on a page, and providing a more detailed web page division into content block data by presenting block boundary range and block variation data.

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