Joint entropy based learning model for image retrieval

Wu, Hao; Li, Yueli; Bi, Xiaohan; Zhang, Linna; Bie, Rongfang; Wang, Yingzhuo

doi:10.1016/j.jvcir.2018.06.021

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…The complexity of code changes/entropy available in the literature is based on the code change process of the software. In [ 60 ], a joint entropy model was used to reduce the possibility of double, useless, and even wrong cases. Thereafter, a database was created that used a large number of photographs.…”

Section: Related Workmentioning

confidence: 99%

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Kumari¹,

Misra

et al. 2019

Entropy

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A software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.

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Section: Related Workmentioning

confidence: 99%

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Kumari¹,

Misra

et al. 2019

Entropy

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“…Keum et al [ 10 ] review and summarize the applications of entropy for water monitoring network design, such as usage of entropy weight method to measure the water quality [ 11 ]. Wu et al [ 12 ], employ a joint entropy-based learning model for optimizing image retrieval. Thus entropy has applications in many fields, including fields like the stock market [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Entropy Churn Metrics for Fault Prediction in Software Systems

Kaur

Chopra

2018

Entropy

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Fault prediction is an important research area that aids software development and the maintenance process. It is a field that has been continuously improving its approaches in order to reduce the fault resolution time and effort. With an aim to contribute towards building new approaches for fault prediction, this paper proposes Entropy Churn Metrics (ECM) based on History Complexity Metrics (HCM) and Churn of Source Code Metrics (CHU). The study also compares performance of ECM with that of HCM. The performance of both these metrics is compared for 14 subsystems of 5different software projects: Android, Eclipse, Apache Http Server, Eclipse C/C++ Development Tooling (CDT), and Mozilla Firefox. The study also analyses the software subsystems on three parameters: (i) distribution of faults, (ii) subsystem size, and (iii) programming language, to determine which characteristics of software systems make HCM or ECM more preferred over others.

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“…For retrieval model construction, Bayesian model, Binary tree, K‐means, and SVM model are frequently used as classic retrieval models. On the foundation of those models, some improved models increase their capabilities.…”

Section: Introductionmentioning

confidence: 99%

Field of experts optimization‐based noisy image retrieval

et al. 2020

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The value of image retrieval has become more and more prominent in the era of big data. However, large numbers of images are missed from current method since the image retrieval precision largely depends on the high quality of images. By common methodology, when the quality of images decreases a little, the accuracy of retrieval would decrease significantly. In particular, it is difficult to retrieve noisy images effectively by conventional approach. Yet large number of the noisy images could not be ignored at the age of data explosion. Aiming at the problem above, we proposed noisy image retrieval model based on field of experts (FoE) optimization. High-quality learning images could be selected by sparse coding, which is based on similarity calculation model, and the multioption filter combination model enhances the power of FoE model. We set up a database containing a large numbers of noisy images. Over this database, adequate groups of experiments are conducted. The verification of the method concluded its effectiveness and superiority.

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Joint entropy based learning model for image retrieval

Cited by 12 publications

References 42 publications

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Entropy Churn Metrics for Fault Prediction in Software Systems

Field of experts optimization‐based noisy image retrieval

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