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Journal articles on the topic 'Feature-based'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Summerfield, Christopher, and Tobias Egner. "Feature-Based Attention and Feature-Based Expectation." Trends in Cognitive Sciences 20, no. 6 (2016): 401–4. http://dx.doi.org/10.1016/j.tics.2016.03.008.

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2

Apel, Sven, Alexander von Rhein, Thomas Thüm, and Christian Kästner. "Feature-interaction detection based on feature-based specifications." Computer Networks 57, no. 12 (2013): 2399–409. http://dx.doi.org/10.1016/j.comnet.2013.02.025.

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3

Ramchandra, Ambika, and Ravindra kumar. "Algorithms of Feature Based and Image Based Face Recognition." Indian Journal of Applied Research 3, no. 12 (2011): 128–30. http://dx.doi.org/10.15373/2249555x/dec2013/34.

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4

Lei, Yan, Huan Xie, Tao Zhang, Meng Yan, Zhou Xu, and Chengnian Sun. "Feature-FL: Feature-Based Fault Localization." IEEE Transactions on Reliability 71, no. 1 (2022): 264–83. http://dx.doi.org/10.1109/tr.2022.3140453.

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5

Ozturk, F., and N. Ozturk. "Feature-based environmental issues: neural network-based feature recognition." International Journal of Vehicle Design 21, no. 2/3 (1999): 190. http://dx.doi.org/10.1504/ijvd.1999.005576.

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6

Ming-liang Gao, Ming-liang Gao, Xiaomin Yang Xiaomin Yang, Yanmei Yu Yanmei Yu, and Daisheng Luo Daisheng Luo. "Photometric invariant feature descriptor based on SIFT." Chinese Optics Letters 10, s1 (2012): S11003–311008. http://dx.doi.org/10.3788/col201210.s11003.

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7

Ashokkumar Lodha, Nishakumari, and Minakshi Somanath Bagad. "Review for Feature Based Image Re-Ranking." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 43–55. https://doi.org/10.70729/ijser15224.

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8

Somanath Bagad, Minakshi, and Sonal P Patil. "Feature Based Image Reranking Using Fusion Weights." International Journal of Scientific Engineering and Research 4, no. 3 (2016): 71–74. https://doi.org/10.70729/ijser15716.

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9

Farah, Qasim Ahmed Alyousuf, and Din Roshidi. "Analysis review on feature-based and word-rule based techniques in text steganography." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 764–70. https://doi.org/10.11591/eei.v9i2.2069.

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This paper presents several techniques used in text steganography in term of feature-based and word-rule based. Additionally, it analyses the performance and the metric evaluation of the techniques used in text steganography. This paper aims to identify the main techniques of text steganography, which are feature-based, and word-rule based, to recognize the various techniques used with them. As a result, the primary technique used in the text steganography was feature-based technique due to its simplicity and secured. Meanwhile, the common parameter metrics utilized in text steganography were

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10

Sloman, S. A. "Feature-Based Induction." Cognitive Psychology 25, no. 2 (1993): 231–80. http://dx.doi.org/10.1006/cogp.1993.1006.

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11

Fischer, J. W., and D. Gathmann. "Feature-based Machining." wt Werkstattstechnik online 99, no. 6 (2009): 432–37. http://dx.doi.org/10.37544/1436-4980-2009-6-432.

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12

Arman, A. C., and G. M. Boynton. "Feature specificity of global-feature-based-attention." Journal of Vision 5, no. 8 (2010): 159. http://dx.doi.org/10.1167/5.8.159.

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13

Zirnsak, M., and F. Hamker. "Global feature-based attention distorts feature space." Journal of Vision 10, no. 7 (2010): 190. http://dx.doi.org/10.1167/10.7.190.

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14

Lu, Yonghe, Wenqiu Liu, and Yanfeng Li. "A Feature Selection Based on Relevance and Redundancy." Journal of Computers 10, no. 4 (2015): 284–91. http://dx.doi.org/10.17706/jcp.10.4.284-291.

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15

He, Shijie. "Image Retrieval Algorithm based on Multi-feature Fusion." International Journal of Scientific Engineering and Research 12, no. 4 (2024): 12–15. https://doi.org/10.70729/se24408105655.

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16

Jiang, Shengyi, and Lianxi Wang. "A clustering-based feature selection via feature separability." Journal of Intelligent & Fuzzy Systems 31, no. 2 (2016): 927–37. http://dx.doi.org/10.3233/jifs-169022.

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17

LI, R. K., C. Y. LIN, and H. H. WU. "Feature modification framework for feature based design systems." International Journal of Production Research 33, no. 2 (1995): 549–63. http://dx.doi.org/10.1080/00207549508930165.

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18

Verma, A. K., and S. Rajotia. "Feature vector: a graph-based feature recognition methodology." International Journal of Production Research 42, no. 16 (2004): 3219–34. http://dx.doi.org/10.1080/00207540410001699408.

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19

Han, JungHyun, and Aristides AG Requicha. "Integration of feature based design and feature recognition." Computer-Aided Design 29, no. 5 (1997): 393–403. http://dx.doi.org/10.1016/s0010-4485(96)00079-6.

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20

Goswami, Saptarsi, Amit Kumar Das, Amlan Chakrabarti, and Basabi Chakraborty. "A feature cluster taxonomy based feature selection technique." Expert Systems with Applications 79 (August 2017): 76–89. http://dx.doi.org/10.1016/j.eswa.2017.01.044.

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21

R, Banupriya, and Dr A. Rajiv Kannan. "A Convolutional Neural Network based Feature Extractor with Discriminant Feature Score for Effective Medical Image Classification." NeuroQuantology 18, no. 7 (2020): 01–08. http://dx.doi.org/10.14704/nq.2020.18.7.nq20185.

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22

Tsai, Meng-Hsiun, Yung-Kuan Chan, An-Mei Hsu, Chia-Yi Chuang, Chuin-Mu Wang, and Po-Whei Huang. "Feature-Based Image Segmentation." Journal of Imaging Science and Technology 57, no. 1 (2013): 1–12. http://dx.doi.org/10.2352/j.imagingsci.technol.2013.57.1.010505.

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23

Sajja, Priti Srinivas. "Feature-based opinion mining." International Journal Of Data Mining And Emerging Technologies 1, no. 1 (2011): 8. http://dx.doi.org/10.5958/j.2249-3212.1.1.2.

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24

Cohen, Maxime C., Ilan Lobel, and Renato Paes Leme. "Feature-Based Dynamic Pricing." Management Science 66, no. 11 (2020): 4921–43. http://dx.doi.org/10.1287/mnsc.2019.3485.

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We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firm needs to price these products to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by applications such as online marketplaces, online flash sales, and loan pricing. We fir

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25

Lim, T., J. Corney, and D. E. R. Clark. "Laminae-based feature recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 9 (2001): 1043–48. http://dx.doi.org/10.1109/34.955117.

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26

Beier, Thaddeus, and Shawn Neely. "Feature-based image metamorphosis." ACM SIGGRAPH Computer Graphics 26, no. 2 (1992): 35–42. http://dx.doi.org/10.1145/142920.134003.

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27

Chu, Veronica C., and Michael D’Zmura. "Tracking feature-based attention." Journal of Neural Engineering 16, no. 1 (2019): 016022. http://dx.doi.org/10.1088/1741-2552/aaed17.

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28

French, Keith, and Xingong Li. "Feature‐based cartographic modelling." International Journal of Geographical Information Science 24, no. 1 (2010): 141–64. http://dx.doi.org/10.1080/13658810802492462.

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29

Huifen, Wang, Zhang Youliang, Cao Jian, Sik-Fun Lee, and Wing-Cheong Kwong. "Feature-based collaborative design." Journal of Materials Processing Technology 139, no. 1-3 (2003): 613–18. http://dx.doi.org/10.1016/s0924-0136(03)00502-8.

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30

de Lasa, Martin, Igor Mordatch, and Aaron Hertzmann. "Feature-based locomotion controllers." ACM Transactions on Graphics 29, no. 4 (2010): 1–10. http://dx.doi.org/10.1145/1778765.1781157.

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31

Yu, Kui, Xianjie Guo, Lin Liu, et al. "Causality-based Feature Selection." ACM Computing Surveys 53, no. 5 (2020): 1–36. http://dx.doi.org/10.1145/3409382.

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32

Turner, Kenneth J. "Validating feature-based specifications." Software: Practice and Experience 36, no. 10 (2006): 999–1027. http://dx.doi.org/10.1002/spe.721.

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33

Chiba, Naoki, Hiroshi Kano, Michihiko Minoh, and Masashi Yasuda. "Feature-based image mosaicing." Systems and Computers in Japan 31, no. 7 (2000): 1–9. http://dx.doi.org/10.1002/(sici)1520-684x(200007)31:7<1::aid-scj1>3.0.co;2-8.

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34

Wang, Lirong, Fang Xu, Jiacai Wang, and Ichiro Hagiwara. "3314 Feature extraction based Efficient Iterative Closest Point Algorithm." Proceedings of Design & Systems Conference 2008.18 (2008): 573–76. http://dx.doi.org/10.1299/jsmedsd.2008.18.573.

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35

SHAMURATOV, Oleksiy. "OBJECT CLUSTERIZATION METHOD IN PICTURES BASED ON FEATURE SELECTION." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 260–64. http://dx.doi.org/10.31891/2307-5732-2022-309-3-260-264.

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The article describes the development of a method that allows you to create clusters based on selecting feature features. In todayʼs world, the entertainment industry on the Internet is developing rapidly, creating a demand for better products. This factor has led to the use of artificial intelligence not only in science but also in entertainment. Currently, applications that allow you to create animations of objects in photos are gaining popularity. This article presents an approach to solving the problem of defining objects for animation. To classify and further identify objects, their chara

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36

Gogoi, Parismita, Debashree Sharma, Rosy Bordoloi, Snigdha Sarma, and Ananya Goswami. "Feature Extraction of Assamese Speech Based One Motion Analysis." Indian Journal Of Science And Technology 16, SP2 (2023): 6–14. http://dx.doi.org/10.17485/ijst/v16isp2.3252.

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37

Liu, Jun Ling, Hong Wei Zhao, Hao Yu Zhao, and Chong Xu Chen. "Image Retrieval Based on Shape Feature and Color Feature." Advanced Materials Research 341-342 (September 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.560.

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Study the retrieval algorithm based on shape feature and based on color features of image retrieval, to improve the accuracy of image retrieval, and to get results consisting with the shape feature and color feature ,this paper proposed new algorithm comprehensivly utilizing the two search algorithms. Through the image retrieval results show, new algorithm obtain results better than two algorithms, and can improve the retrieval precision.

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38

Fang, Ming W. H., and Taosheng Liu. "Feature Competition Modulates the Profile of Feature-based Attention." Journal of Vision 21, no. 9 (2021): 2349. http://dx.doi.org/10.1167/jov.21.9.2349.

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39

Ramli, Roziana, Mohd Yamani Idna Idris, Khairunnisa Hasikin, et al. "Feature-Based Retinal Image Registration Using D-Saddle Feature." Journal of Healthcare Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/1489524.

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Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Sad

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40

Nie, Weizhi, Anan Liu, Yuting Su, and Sha Wei. "Multi-view feature extraction based on slow feature analysis." Neurocomputing 252 (August 2017): 49–57. http://dx.doi.org/10.1016/j.neucom.2016.01.125.

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41

Störmer, Viola S., and George A. Alvarez. "Feature-Based Attention Elicits Surround Suppression in Feature Space." Current Biology 24, no. 17 (2014): 1985–88. http://dx.doi.org/10.1016/j.cub.2014.07.030.

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42

Verikas, A., M. Bacauskiene, D. Valincius, and A. Gelzinis. "Predictor output sensitivity and feature similarity-based feature selection." Fuzzy Sets and Systems 159, no. 4 (2008): 422–34. http://dx.doi.org/10.1016/j.fss.2007.05.020.

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43

Ovtcharova, Jivka, Gerhard Pahl, and Joachim Rix. "A proposal for feature classification in feature-based design." Computers & Graphics 16, no. 2 (1992): 187–95. http://dx.doi.org/10.1016/0097-8493(92)90046-x.

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44

Zhou, Hongfang, Lei An, and Rourou Zhu. "A grouping feature selection method based on feature interaction." Intelligent Data Analysis 27, no. 2 (2023): 361–77. http://dx.doi.org/10.3233/ida-226551.

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Feature interaction is crucial in the process of feature selection. In this paper, a grouping feature selection method based on feature interaction (GFS-NPIS) is proposed. Firstly, a new evaluation function measuring feature interaction is proposed. Secondly, a grouping strategy based on approximate Markov blanket is used to remove strong redundant features. Lastly, a new feature selection method called as GFS-NPIS is given. In order to verify the effectiveness of our method, we compare GFS-NPIS with other eight representative ones on three classifiers (SVM, KNN and CART). The experimental res

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45

Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, et al. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (2022): 131–41. http://dx.doi.org/10.53106/199115992022123306011.

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<p>Feature selection is an important part of data preprocessing. Feature selection algorithms that use mutual information as evaluation can effectively handle different types of data, so it has been widely used. However, the potential relationship between relevance and redundancy in the evaluation criteria is often ignored, so that effective feature subsets cannot be selected. Optimize the evaluation criteria of the mutual information feature selection algorithm and propose a mutual information feature selection algorithm based on dynamic penalty factors (Dynamic Penalty Factor Mutual In

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46

Chunxia Wang, Chunxia Wang, Qiuyu Zhang Chunxia Wang, and Yan Yan Qiuyu Zhang. "Differentially Private Feature Selection Based on Dynamic Relevance for Correlated Data." 電腦學刊 34, no. 1 (2023): 157–73. http://dx.doi.org/10.53106/199115992023023401012.

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<p>Traditional feature selection methods are only concerned with high relevance between selected features and classes and low redundancy among features, ignoring their interrelations which partly weak classification performance. This paper developed a dynamic relevance strategy to measure the dependency among them, where the relevance of each candidate feature is updated dynamically when a new feature is selected. Protecting sensitive information has become an important issue when executing feature selection. However, existing differentially private machine learning algorithms have seldo

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47

Shah, Hetal, and Mehfuza S. Holia. "Multi-dimensional CNN Based Feature Extraction with Feature Fusion and SVM for Human Activity Recognition in Surveillance Videos." Indian Journal Of Science And Technology 17, no. 21 (2024): 2177–98. http://dx.doi.org/10.17485/ijst/v17i21.3203.

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Background/Objectives: The accurate recognition of human activities from video sequences is very challenging due to low resolution, cluttered background, partial occlusion, and different viewpoints. Machine learning (ML) based automated HAR from surveillance videos is required with the fusion of various feature extraction techniques. Methods: In this paper, SVM with feature fusion is utilized for automatic recognition from surveillance videos. A Histogram of Oriented Gradient (HOG) is used to segment the frame to differentiate humans from other objects or background noise in the input video fr

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48

Lu, J., R. Yakupov, C. Lozar, L. Chang, T. Ernst, and L. Itti. "Feature-based attention is also object-based." Journal of Vision 5, no. 8 (2005): 1034. http://dx.doi.org/10.1167/5.8.1034.

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49

Lu, J., and L. Itti. "Feature-based attention is not object-based." Journal of Vision 6, no. 6 (2010): 786. http://dx.doi.org/10.1167/6.6.786.

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50

Tran, Chi-Kien. "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model." International Journal of Machine Learning and Computing 9, no. 3 (2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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