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Journal articles on the topic 'Feature-based'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

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

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2

Ramchandra, Ambika, and Ravindra kumar. "Algorithms of Feature Based and Image Based Face Recognition." Indian Journal of Applied Research 3, no. 12 (October 1, 2011): 128–30. http://dx.doi.org/10.15373/2249555x/dec2013/34.

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3

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

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4

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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5

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 (March 2022): 264–83. http://dx.doi.org/10.1109/tr.2022.3140453.

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6

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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7

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

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8

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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9

Arman, A. C., and G. M. Boynton. "Feature specificity of global-feature-based-attention." Journal of Vision 5, no. 8 (March 16, 2010): 159. http://dx.doi.org/10.1167/5.8.159.

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10

Zirnsak, M., and F. Hamker. "Global feature-based attention distorts feature space." Journal of Vision 10, no. 7 (August 2, 2010): 190. http://dx.doi.org/10.1167/10.7.190.

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11

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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12

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 (July 31, 2020): 01–08. http://dx.doi.org/10.14704/nq.2020.18.7.nq20185.

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13

Jiang, Shengyi, and Lianxi Wang. "A clustering-based feature selection via feature separability." Journal of Intelligent & Fuzzy Systems 31, no. 2 (July 22, 2016): 927–37. http://dx.doi.org/10.3233/jifs-169022.

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14

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 (February 1995): 549–63. http://dx.doi.org/10.1080/00207549508930165.

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15

Verma, A. K., and S. Rajotia. "Feature vector: a graph-based feature recognition methodology." International Journal of Production Research 42, no. 16 (August 15, 2004): 3219–34. http://dx.doi.org/10.1080/00207540410001699408.

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16

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

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17

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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18

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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19

SHAMURATOV, Oleksiy. "OBJECT CLUSTERIZATION METHOD IN PICTURES BASED ON FEATURE SELECTION." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (May 26, 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 characteristics should be determined. This is one of the options for abstraction, in which the input set of properties of the object is reduced to the minimum required number of features by which you can identify the object. The algorithm can be used to determine the main features of objects, such as area and perimeter, radii of inscribed and circumscribed circles, sides of the described rectangle, number and relative position of angles, gradient of the object histogram. Based on these features clustering and classification of the image are implemented. The artificial neural network was trained on image samples, each class contained from 2528 to 16185 images of 64×64 pixels. 1000 images of objects of each class were then selected for testing. The success of recognition based on a convolutional neural network was evaluated. According to the results, we can conclude that the smaller the invariance of the class, the greater the accuracy of recognition. The amount of data in the training sample has little effect on the accuracy of the algorithm. After calculating the intensity gradient, you should divide the image into a cell and build a histogram of the gradient object for each pocket of cell; the histogram module corresponds to the intensity gradient at the point.

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20

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 (January 1, 2013): 1–12. http://dx.doi.org/10.2352/j.imagingsci.technol.2013.57.1.010505.

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21

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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22

Cohen, Maxime C., Ilan Lobel, and Renato Paes Leme. "Feature-Based Dynamic Pricing." Management Science 66, no. 11 (November 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 first consider a multidimensional version of binary search over polyhedral sets and show that it has a worst-case regret which is exponential in the dimension of the feature space. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Löwner-John ellipsoids. We show that this algorithm has a worst-case regret which is quadratic in the dimension of the feature space and logarithmic in the time horizon. We also show how to adapt our algorithm to the case where valuations are noisy. Finally, we present computational experiments to illustrate the performance of our algorithm. This paper was accepted by Yinyu Ye, optimization.

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23

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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24

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

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25

Chu, Veronica C., and Michael D’Zmura. "Tracking feature-based attention." Journal of Neural Engineering 16, no. 1 (January 9, 2019): 016022. http://dx.doi.org/10.1088/1741-2552/aaed17.

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26

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

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27

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 (August 2003): 613–18. http://dx.doi.org/10.1016/s0924-0136(03)00502-8.

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28

de Lasa, Martin, Igor Mordatch, and Aaron Hertzmann. "Feature-based locomotion controllers." ACM Transactions on Graphics 29, no. 4 (July 26, 2010): 1–10. http://dx.doi.org/10.1145/1778765.1781157.

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29

Yu, Kui, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, and Xindong Wu. "Causality-based Feature Selection." ACM Computing Surveys 53, no. 5 (October 15, 2020): 1–36. http://dx.doi.org/10.1145/3409382.

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30

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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31

Chiba, Naoki, Hiroshi Kano, Michihiko Minoh, and Masashi Yasuda. "Feature-based image mosaicing." Systems and Computers in Japan 31, no. 7 (July 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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32

Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, Xin Sui Hai-Long Zhao, Yong-Qi Guo Xin Sui, Mei-Ling Zheng Yong-Qi Guo, and Li Li Mei-Ling Zheng. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (December 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 Information Feature Selection Algorithm, DPMFS). The penalty factor is dynamically calculated with different selected features, so as to achieve a relative balance between relevance and redundancy, and effectively play the synergy between relevance and redundancy, and select a suitable feature subset. Experimental results verify that the DPMFS algorithm can effectively improve the classification accuracy of the feature selection algorithm. Compared with the traditional chi-square, MIM and MIFS feature selection algorithms, the average classification accuracy of the random forest classifier for the six standard datasets is increased by 3.73%, 3.51% and 2.44%, respectively.</p> <p> </p>

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33

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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34

Fang, Ming W. H., and Taosheng Liu. "Feature Competition Modulates the Profile of Feature-based Attention." Journal of Vision 21, no. 9 (September 27, 2021): 2349. http://dx.doi.org/10.1167/jov.21.9.2349.

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35

Ramli, Roziana, Mohd Yamani Idna Idris, Khairunnisa Hasikin, Noor Khairiah A. Karim, Ainuddin Wahid Abdul Wahab, Ismail Ahmedy, Fatimah Ahmedy, Nahrizul Adib Kadri, and Hamzah Arof. "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-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

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36

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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37

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

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38

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 (February 2008): 422–34. http://dx.doi.org/10.1016/j.fss.2007.05.020.

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39

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

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40

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 (June 2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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41

Khankasikam, Krisda. "A New Fragile Watermarking Scheme Based on Wavelet Edge Feature." International Journal of Future Computer and Communication 4, no. 4 (2015): 270–74. http://dx.doi.org/10.7763/ijfcc.2015.v4.400.

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42

Fanqi Meng, Fanqi Meng, Yujie Zheng Fanqi Meng, Jingdong Wang Yujie Zheng, and Songbin Bao Jingdong Wang. "Prediction of Academic Formulaic Language based on Multi-feature Fusion." 電腦學刊 33, no. 3 (June 2022): 035–47. http://dx.doi.org/10.53106/199115992022063303003.

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<p>Academic formulaic language is multi-word combinations with specific functions and semantics, which are important to improve the idiomaticity, fluency and logic of machine translation, intelligent question answering, automatic summarization, etc. In order to narrow the search range of the corpus and extract academic formulaic language more efficiently, this paper proposes a prediction model of academic formulaic language based on multi-feature fusion. The semantic features and part-of-speech features of the academic formulaic language are extracted separately, and then the late fusion method is used to learn multiple features and predict whether formulaic language is included in the sentence. Experimental results show that the late fusion method based on part-of-speech features and semantic features has the best predictive effect among the four fusion methods, which lays the foundation for further efficient recognition of academic formulaic language.</p> <p> </p>

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43

Li, Zhen, Yuren Du, Miaomiao Zhu, Shi Zhou, Seiichi Serikawa, and Lifeng Zhang. "A High-accuracy and Semi-dense Feature-based VSLAM System." Journal of the Institute of Industrial Applications Engineers 9, no. 4 (October 25, 2021): 124–30. http://dx.doi.org/10.12792/jiiae.9.124.

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44

A, Adarsh, Akshatha S. Kumar, and Pranav P. M. Dr Saravana Balaji B. ShruthiShree S. H. "Multifactor Based Top K Feature Extraction Using Summarized Customer Reviews." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 999–1003. http://dx.doi.org/10.31142/ijtsrd14183.

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45

CHEN, Jin, and Guo BI. "Cyclostationary Analysis based Fault Feature Extracting of Rolling Element Bearing." Proceedings of the Symposium on Evaluation and Diagnosis 2006.5 (2006): 1–6. http://dx.doi.org/10.1299/jsmesed.2006.5.1.

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46

Ma, Y. S., N. Sajadfar, and L. Campos Triana. "A Feature-Based Semantic Model for Automatic Product Cost Estimation." International Journal of Engineering and Technology 6, no. 2 (2014): 109–13. http://dx.doi.org/10.7763/ijet.2014.v6.676.

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47

Banu, Rubeena, and M. H. Sidram. "Window Based Min-Max Feature Extraction for Visual Object Tracking." Indian Journal Of Science And Technology 15, no. 40 (October 27, 2022): 2047–55. http://dx.doi.org/10.17485/ijst/v15i40.1395.

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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 (September 1, 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 (March 24, 2010): 786. http://dx.doi.org/10.1167/6.6.786.

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50

Song, Tao, Bing Chen, Fu M. Zhao, Zheng Huang, and Mu J. Huang. "Research on image feature matching algorithm based on feature optical flow and corner feature." Journal of Engineering 2020, no. 13 (July 1, 2020): 529–34. http://dx.doi.org/10.1049/joe.2019.1156.

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