Word ranking in a single document by Jensen–Shannon divergence

Mehri, Ali; Jamaati, Maryam; Mehri, Hassan

doi:10.1016/j.physleta.2015.04.030

Cited by 21 publications

(12 citation statements)

References 18 publications

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“…𝑛 𝑘 indicates the number of pixels on the gray surface k, and L represents the number of gray levels. M, N also represents the number of rows and columns in the image, respectively [39].…”

Section: Jensen -Tsallis Divergencementioning

confidence: 99%

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

2022

IJE

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Texture image segmentation plays an important role in various computer vision tasks. Active contour models are one of the most efficient and popular methods for identifying the purpose and segmentation of objects in the image. This paper presents a parametric active contour model (PACM) with a robust minimization framework based on image texture energy. First, the texture features of the original image are extracted using gray level co-occurrence matrix (GLCM). Subsequently, based on the GLCM texture features inside and outside the active contour, Jensen-Tsallis divergence of energies is calculated. The Jensen-Tsallis divergence is added to the parametric active contour using the balloon equation. The divergence is maximum at the boundary between the foreground and background of the image, which results in minimizing the active contour equation at the boundary of the target object. This global minimization energy function with texture feature can avoid the existence of local minima in the PACM models. Also, as opposed to previous models, the proposed model only requires the initial contour and is not dependent on the distance of the initial contour from the target object. In terms of segmentation accuracy and efficiency, experiments with synthetic and natural images demonstrate that the proposed approach obtains more satisfactory results than the previous state-of-the-art methods.

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Section: Jensen -Tsallis Divergencementioning

confidence: 99%

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

2022

IJE

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“…Jensen-Shannon Divergence (JSD) (Endres & Schindelin 2003, Ôsterreicher & Vajda 2003 is the measure of (dis)similarity between two probability distributions. Mehri et al (2015) utilized JSD for extracting keywords in a text. They defined a spatial distribution of words in a text by applying boxcounting method.…”

Section: Jensen-shannon Divergencementioning

confidence: 99%

A Comparative Evaluation of Different Keyword Extraction Techniques

Bisht

2021

International Journal of Information Retrieval Research

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Retrieving keywords in a text is attracting researchers for a long time as it forms a base for many natural language applications like information retrieval, text summarization, document categorization etc. A text is a collection of words that represent the theme of the text naturally and to bring the naturalism under certain rules is itself a challenging task. In the present paper, the authors evaluate different spatial distribution based keyword extraction methods available in the literature on three standard scientific texts. The authors choose the first few high-frequency words for evaluation to reduce the complexity as all the methods are somehow based on frequency. The authors find that the methods are not providing good results particularly in the case of the first few retrieved words. Thus, the authors propose a new measure based on frequency, inverse document frequency, variance, and Tsallis entropy. Evaluation of different methods is done on the basis of precision, recall, and F-measure. Results show that the proposed method provides improved results.

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“…The similarity between two questions q i ¼ {z i1 , z i2 , …, z im } and q j ¼ {z j1 , z j2 , …, z jm } is derived based on the Jensen-Shannon divergence (Mehri et al, 2015) and is defined as:…”

Section: Modeling Questionsmentioning

confidence: 99%

Extracting core questions in community question answering based on particle swarm optimization

Chen

2019

DTA

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Purpose A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These core questions should cover the main content of the original question set. There should be low redundancy within the core questions and a consistent distribution with the original question set. The paper aims to discuss these issues. Design/methodology/approach In the paper, a method named QueExt method for extracting core questions is proposed. First, questions are modeled using a biterm topic model. Then, these questions are clustered based on particle swarm optimization (PSO). With the clustering results, the number of core questions to be extracted from each cluster can be determined. Afterwards, the multi-objective PSO algorithm is proposed to extract the core questions. Both PSO algorithms are integrated with operators in genetic algorithms to avoid the local optimum. Findings Extensive experiments on real data collected from the famous CQA website Zhihu have been conducted and the experimental results demonstrate the superior performance over other benchmark methods. Research limitations/implications The proposed method provides new insight into and enriches research on information overload in CQA. It performs better than other methods in extracting core short text documents, and thus provides a better way to extract core data. The PSO is a novel method used for selecting core questions. The research on the application of the PSO model is expanded. The study also contributes to research on PSO-based clustering. With the integration of K-means++, the key parameter number of clusters is optimized. Originality/value The novel core question extraction method in CQA is proposed, which provides a novel and efficient way to alleviate the question overload. The PSO model is extended and novelty used in selecting core questions. The PSO model is integrated with K-means++ method to optimize the number of clusters, which is just the key parameter in text clustering based on PSO. It provides a new way to cluster texts.

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Word ranking in a single document by Jensen–Shannon divergence

Cited by 21 publications

References 18 publications

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

A Comparative Evaluation of Different Keyword Extraction Techniques

Extracting core questions in community question answering based on particle swarm optimization

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