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Journal articles on the topic 'Retrieval-based learning'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Karpicke, Jeffrey D. "Retrieval-Based Learning." Current Directions in Psychological Science 21, no. 3 (May 30, 2012): 157–63. http://dx.doi.org/10.1177/0963721412443552.

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2

Fazio, Lisa K., and Elizabeth J. Marsh. "Retrieval-Based Learning in Children." Current Directions in Psychological Science 28, no. 2 (January 7, 2019): 111–16. http://dx.doi.org/10.1177/0963721418806673.

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Testing oneself with flash cards, using a clicker to respond to a teacher’s questions, and teaching another student are all effective ways to learn information. These learning strategies work, in part, because they require the retrieval of information from memory, a process known to enhance later memory. However, little research has directly examined retrieval-based learning in children. We review the emerging literature on the benefits of retrieval-based learning for preschool and elementary school students and draw on other literatures for further insights. We reveal clear evidence for the benefits of retrieval-based learning in children (starting in infancy). However, we know little about the developmental trajectory. Overall, the benefits are largest when the initial retrieval practice is effortful but successful.
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3

Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

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Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy
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4

Blunt, Janell R., and Jeffrey D. Karpicke. "Learning with retrieval-based concept mapping." Journal of Educational Psychology 106, no. 3 (2014): 849–58. http://dx.doi.org/10.1037/a0035934.

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5

Sanders, Lia Lira Olivier, Randal Pompeu Ponte, Antônio Brazil Viana Júnior, Arnaldo Aires Peixoto Junior, Marcos Kubrusly, and Antônio Miguel Furtado Leitão. "Retrieval-Based Learning in Neuroanatomy Classes." Revista Brasileira de Educação Médica 43, no. 4 (December 2019): 92–98. http://dx.doi.org/10.1590/1981-52712015v43n4rb20180184ingles.

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ABSTRACT Medical schools are continuously challenged to develop teaching modalities that improve understanding and retention of anatomical knowledge. Traditionally, learning has been regarded as the encoding of new knowledge, whereas retrieval has been considered a means for assessing learning. A solid body of research demonstrates that retrieval practice is a way to promote learning that is robust, durable, and transferable to new contexts. It involves having learners set aside the material they are learning and practice actively reconstructing it on their own. A general challenge is to develop ways to implement retrieval-based learning in educational settings. We developed a pedagogical approach that implements retrieval-based learning in practical neuroanatomy classes, which differs from usual neuroanatomy teaching in that it actively engages students through active learning. It requires students to retrieve anatomical knowledge in oral and written form, as well as to identify structures in cadaveric material. Practical anatomy classes have traditionally relied on students’ passive exposure to cadaveric material, with the lecturer pointing to and naming anatomical structures. Since August 2014, we have been applying retrieval practice in neuroanatomy classes. A total of 720 students were included in the study. Student performance one week after the practical lesson was higher in the traditional method group than in the retrieval-based learning group (p < 0.0001, effect size = 0.60). Four weeks after the intervention, however, the performance of students who learned using a retrieval-based approach was higher than that of students passively exposed to the learning material (p < 0.0001, effect size = 0.75). Taken together, our results suggest that retrieval-based learning has a greater effect on long-term retention. Retrieval-based learning is easy to apply and cost-effective. It can be implemented in nearly any educational setting. We hope that our report may inspire educators to adopt retrieval practice approaches and seek ways to apply methods from learning research in actual classrooms.
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6

Li, Yueli, Rongfang Bie, Chenyun Zhang, Zhenjiang Miao, Yuqi Wang, Jiajing Wang, and Hao Wu. "Optimized learning instance-based image retrieval." Multimedia Tools and Applications 76, no. 15 (September 20, 2016): 16749–66. http://dx.doi.org/10.1007/s11042-016-3950-9.

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7

B, Gomathi. "Semantic Web Application in E-learning Using Protege based on Information Retrieval." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1847–55. http://dx.doi.org/10.5373/jardcs/v12sp7/20202297.

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8

Aziz, Noor Azizah Bt. "Choosing Appropriate Retrieval based Learning Elements among Students in Java Programming Course." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 5448–55. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020251.

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9

Ramirez-Arellano, Aldo, Juan Bory-Reyes, and Luis Manuel Hernández-Simón. "Learning Object Retrieval and Aggregation Based on Learning Styles." Journal of Educational Computing Research 55, no. 6 (December 6, 2016): 757–88. http://dx.doi.org/10.1177/0735633116681303.

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The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.
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10

Karpicke, Jeffrey D., and Phillip J. Grimaldi. "Retrieval-Based Learning: A Perspective for Enhancing Meaningful Learning." Educational Psychology Review 24, no. 3 (August 4, 2012): 401–18. http://dx.doi.org/10.1007/s10648-012-9202-2.

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11

Wu, Hui, Min Wang, Wengang Zhou, Yang Hu, and Houqiang Li. "Learning Token-Based Representation for Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2703–11. http://dx.doi.org/10.1609/aaai.v36i3.20173.

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In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.
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12

Yang, Jianyu, and Haoran Xu. "Metric learning based object recognition and retrieval." Neurocomputing 190 (May 2016): 70–81. http://dx.doi.org/10.1016/j.neucom.2016.01.032.

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13

Lu, Ke, and Xiaofei He. "Image retrieval based on incremental subspace learning." Pattern Recognition 38, no. 11 (November 2005): 2047–54. http://dx.doi.org/10.1016/j.patcog.2005.05.005.

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14

徐, 海蛟. "Deep Learning Based Semantic Scene Image Retrieval." Computer Science and Application 09, no. 08 (2019): 1561–68. http://dx.doi.org/10.12677/csa.2019.98175.

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15

Schwoebel, John, Acasia K. Depperman, and Jessica L. Scott. "Distinct episodic contexts enhance retrieval-based learning." Memory 26, no. 9 (April 12, 2018): 1291–96. http://dx.doi.org/10.1080/09658211.2018.1464190.

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16

Wang, Wei, Xiaoyan Yang, Beng Chin Ooi, Dongxiang Zhang, and Yueting Zhuang. "Effective deep learning-based multi-modal retrieval." VLDB Journal 25, no. 1 (July 19, 2015): 79–101. http://dx.doi.org/10.1007/s00778-015-0391-4.

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17

Karpicke, Jeffrey D., Janell R. Blunt, Megan A. Smith, and Stephanie S. Karpicke. "Retrieval-based learning: The need for guided retrieval in elementary school children." Journal of Applied Research in Memory and Cognition 3, no. 3 (September 2014): 198–206. http://dx.doi.org/10.1037/h0101802.

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18

Karpicke, Jeffrey D., Janell R. Blunt, Megan A. Smith, and Stephanie S. Karpicke. "Retrieval-based learning: The need for guided retrieval in elementary school children." Journal of Applied Research in Memory and Cognition 3, no. 3 (September 2014): 198–206. http://dx.doi.org/10.1016/j.jarmac.2014.07.008.

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19

Lv, Yuanhai, Chongyan Wang, Wanteng Yuan, Xiaohao Qian, Wujun Yang, and Wanqing Zhao. "Transformer-Based Distillation Hash Learning for Image Retrieval." Electronics 11, no. 18 (September 6, 2022): 2810. http://dx.doi.org/10.3390/electronics11182810.

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In recent years, Transformer has become a very popular architecture in deep learning and has also achieved the same state-of-the-art performance as convolutional neural networks on multiple image recognition baselines. Transformer can obtain global perceptual fields through a self-attention mechanism and can enhance the weights of unique discriminable features for image retrieval tasks to improve the retrieval quality. However, Transformer is computationally intensive and finds it difficult to satisfy real-time requirements when used for retrieval tasks. In this paper, we propose a Transformer-based image hash learning framework and compress the constructed framework to perform efficient image retrieval using knowledge distillation. By combining the self-attention mechanism of the Transformer model, the image hash code is enabled to be global and unique. At the same time, this advantage is instilled into the efficient lightweight model by knowledge distillation, thus reducing the computational complexity and having the advantage of an attention mechanism in the Transformer. The experimental results on the MIRFlickr-25K dataset and NUS-WIDE dataset show that our approach can effectively improve the accuracy and efficiency of image retrieval.
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20

Singh, Vibhav Prakash, Rajeev Srivastava, Yadunath Pathak, Shailendra Tiwari, and Kuldeep Kaur. "Content-based image retrieval based on supervised learning and statistical-based moments." Modern Physics Letters B 33, no. 19 (July 8, 2019): 1950213. http://dx.doi.org/10.1142/s0217984919502130.

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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.
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21

McDermott, Kathleen B. "Practicing Retrieval Facilitates Learning." Annual Review of Psychology 72, no. 1 (January 4, 2021): 609–33. http://dx.doi.org/10.1146/annurev-psych-010419-051019.

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How do we go about learning new information? This article reviews the importance of practicing retrieval of newly experienced information if one wants to be able to retrieve it again in the future. Specifically, practicing retrieval shortly after learning can slow the forgetting process. This benefit can be seen across various material types, and it seems prevalent in all ages and learner abilities and on all types of test. It can also be used to enhance student learning in a classroom setting. I review theoretical understanding of this phenomenon (sometimes referred to as the testing effect or as retrieval-based learning) and consider directions for future research.
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22

Nishizaki, Yohei, Ryoichi Horisaki, Katsuhisa Kitaguchi, Mamoru Saito, and Jun Tanida. "Analysis of non-iterative phase retrieval based on machine learning." Optical Review 27, no. 1 (January 9, 2020): 136–41. http://dx.doi.org/10.1007/s10043-019-00574-8.

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AbstractIn this paper, we analyze a machine-learning-based non-iterative phase retrieval method. Phase retrieval and its applications have been attractive research topics in optics and photonics, for example, in biomedical imaging, astronomical imaging, and so on. Most conventional phase retrieval methods have used iterative processes to recover phase information; however, the calculation speed and convergence with these methods are serious issues in real-time monitoring applications. Machine-learning-based methods are promising for addressing these issues. Here, we numerically compare conventional methods and a machine-learning-based method in which a convolutional neural network is employed. Simulations with several conditions show that the machine-learning-based method realizes fast and robust phase recovery compared with the conventional methods. We also numerically demonstrate machine-learning-based phase retrieval from noisy measurements with a noisy training data set for improving the noise robustness. The machine-learning-based approach used in this study may increase the impact of phase retrieval, which is useful in various fields, where phase retrieval has been used as a fundamental tool.
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23

Mu, Pan-pan, San-yuan Zhang, Yin Zhang, Xiu-zi Ye, and Xiang Pan. "Image-based 3D model retrieval using manifold learning." Frontiers of Information Technology & Electronic Engineering 19, no. 11 (November 2018): 1397–408. http://dx.doi.org/10.1631/fitee.1601764.

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24

Zheng, Xiyuan, Wei Zhu, Zhenmei Yu, and Meijia Zhang. "Semi-Supervised Learning Based Semantic Cross-Media Retrieval." IEEE Access 9 (2021): 75049–57. http://dx.doi.org/10.1109/access.2021.3080976.

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25

Dalvi, Nikhil. "Content Based Image retrieval system using machine learning." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 538–41. http://dx.doi.org/10.22214/ijraset.2019.6095.

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26

Xu, Qingyong. "Feature Fusion Based Image Retrieval Using Deep Learning." Journal of Information and Computational Science 12, no. 6 (April 10, 2015): 2361–73. http://dx.doi.org/10.12733/jics20105681.

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27

Joo-Hwee Lim, Wu Jian Kang, S. Singh, and D. Narasimhalu. "Learning similarity matching in multimedia content-based retrieval." IEEE Transactions on Knowledge and Data Engineering 13, no. 5 (2001): 846–50. http://dx.doi.org/10.1109/69.956107.

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28

Wang, Yaxiong, Li Zhu, Xueming Qian, and Junwei Han. "Joint Hypergraph Learning for Tag-Based Image Retrieval." IEEE Transactions on Image Processing 27, no. 9 (September 2018): 4437–51. http://dx.doi.org/10.1109/tip.2018.2837219.

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29

Ma, Qing, Cong Bai, Jinglin Zhang, Zhi Liu, and Shengyong Chen. "Supervised learning based discrete hashing for image retrieval." Pattern Recognition 92 (August 2019): 156–64. http://dx.doi.org/10.1016/j.patcog.2019.03.022.

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30

Jiang, Bin, Jiachen Yang, Zhihan Lv, Kun Tian, Qinggang Meng, and Yan Yan. "Internet cross-media retrieval based on deep learning." Journal of Visual Communication and Image Representation 48 (October 2017): 356–66. http://dx.doi.org/10.1016/j.jvcir.2017.02.011.

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31

Wu, Hao, Yueli Li, Xiaohan Bi, Linna Zhang, Rongfang Bie, and Yingzhuo Wang. "Joint entropy based learning model for image retrieval." Journal of Visual Communication and Image Representation 55 (August 2018): 415–23. http://dx.doi.org/10.1016/j.jvcir.2018.06.021.

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32

Tzelepi, Maria, and Anastasios Tefas. "Deep convolutional learning for Content Based Image Retrieval." Neurocomputing 275 (January 2018): 2467–78. http://dx.doi.org/10.1016/j.neucom.2017.11.022.

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33

Srinivas, M., R. Ramu Naidu, C. S. Sastry, and C. Krishna Mohan. "Content based medical image retrieval using dictionary learning." Neurocomputing 168 (November 2015): 880–95. http://dx.doi.org/10.1016/j.neucom.2015.05.036.

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34

Shang, Fei, Huaxiang Zhang, Lei Zhu, and Jiande Sun. "Adversarial cross-modal retrieval based on dictionary learning." Neurocomputing 355 (August 2019): 93–104. http://dx.doi.org/10.1016/j.neucom.2019.04.041.

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35

Saritha, R. Rani, Varghese Paul, and P. Ganesh Kumar. "Content based image retrieval using deep learning process." Cluster Computing 22, S2 (February 7, 2018): 4187–200. http://dx.doi.org/10.1007/s10586-018-1731-0.

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36

Onoda, Takashi, Hiroshi Murata, and Seiji Yamada. "SVM-based Interactive Document Retrieval with Active Learning." New Generation Computing 26, no. 1 (November 2007): 49–61. http://dx.doi.org/10.1007/s00354-007-0034-4.

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37

., Renuka Devi S. M. "LEARNING IN CONTENT BASED IMAGE RETRIEVAL : A REVIEW." International Journal of Research in Engineering and Technology 05, no. 02 (February 25, 2016): 97–104. http://dx.doi.org/10.15623/ijret.2016.0502018.

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38

Liu, Hui, Cai Ming Zhang, and Hua Han. "Medical Image Retrieval Based on Semi-Supervised Learning." Advanced Materials Research 108-111 (May 2010): 201–6. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.201.

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Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.
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39

Oommen, B. J. "Fast Learning Automaton-Based Image Examination and Retrieval." Computer Journal 36, no. 6 (June 1, 1993): 542–53. http://dx.doi.org/10.1093/comjnl/36.6.542.

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40

Li, Jing, and Nigel M. Allinson. "Long-term learning in content-based image retrieval." International Journal of Imaging Systems and Technology 18, no. 2-3 (August 11, 2008): 160–69. http://dx.doi.org/10.1002/ima.20148.

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41

Sivakumar, M., N. M. Saravana Kumar, and N. Karthikeyan. "An Efficient Deep Learning-based Content-based Image Retrieval Framework." Computer Systems Science and Engineering 43, no. 2 (2022): 683–700. http://dx.doi.org/10.32604/csse.2022.021459.

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42

Kokare, Manesh, and Nilesh Bhosle. "Random forest-based active learning for content-based image retrieval." International Journal of Intelligent Information and Database Systems 13, no. 1 (2020): 72. http://dx.doi.org/10.1504/ijiids.2020.10030218.

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Bhosle, Nilesh, and Manesh Kokare. "Random forest-based active learning for content-based image retrieval." International Journal of Intelligent Information and Database Systems 13, no. 1 (2020): 72. http://dx.doi.org/10.1504/ijiids.2020.108223.

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44

Jin, Cong, and Shu-Wei Jin. "Content-based image retrieval model based on cost sensitive learning." Journal of Visual Communication and Image Representation 55 (August 2018): 720–28. http://dx.doi.org/10.1016/j.jvcir.2018.08.009.

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45

Yan, Aijun, Hang Yu, and Dianhui Wang. "Case-based reasoning classifier based on learning pseudo metric retrieval." Expert Systems with Applications 89 (December 2017): 91–98. http://dx.doi.org/10.1016/j.eswa.2017.07.022.

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46

Chang, Hong, and Dit-Yan Yeung. "Kernel-based distance metric learning for content-based image retrieval." Image and Vision Computing 25, no. 5 (May 2007): 695–703. http://dx.doi.org/10.1016/j.imavis.2006.05.013.

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47

Xiang, Jun, Ruru Pan, and Weidong Gao. "Mélange fabric image retrieval based on soft similarity learning." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502210888. http://dx.doi.org/10.1177/15589250221088896.

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Fabric image retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. Compared with common image retrieval, fabric image retrieval has high requirements for results. To address the actual needs of the industry for Mélange fabric retrieval, we propose a novel framework for efficient and accurate fabric retrieval. We first introduce a quantified similarity definition, soft similarity, to measure the fine-grained pairwise similarity and design a CNN for fabric image representation. An objective function, which consists of three losses: soft similarity loss for preserving the similarity, cross-entropy loss for image representation, and quantization loss for controlling the quality of hash code, is used to drive the learning of the model. Experimental results demonstrate that the proposed method can not only achieve effective feature learning and hashing learning, but also effectively work on smaller datasets. Comparative experiments illustrate that the proposed method outperforms the compared methods.
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48

Li, Peng. "Image Color Recognition and Optimization Based on Deep Learning." Wireless Communications and Mobile Computing 2022 (August 9, 2022): 1–7. http://dx.doi.org/10.1155/2022/7226598.

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In order to solve the problem of image color recognition, this paper proposes a method of image color recognition and optimization based on deep learning and designs a postprocessing framework based on word bag model (bow). The framework uses CNN features and calculates feature similarity. The image sets with high similarity are input into the image classifier trained by bow clustering model as the preliminary retrieval results. The retrieval results are the categories with the largest number of images. The experimental results show that the image retrieval accuracy of the framework is 90.4% based on the same data set and classification category, which is 10% higher than the image retrieval algorithm based on CNN features. Conclusion. The color matching degree between the image color and the image to be retrieved has been greatly improved.
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49

Li, Yinhai, Fei Wang, and Xinhua Hu. "Deep-Learning-Based 3D Reconstruction: A Review and Applications." Applied Bionics and Biomechanics 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/3458717.

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In recent years, deep learning models have been widely used in 3D reconstruction fields and have made remarkable progress. How to stimulate deep academic interest to effectively manage the explosive augmentation of 3D models has been a research hotspot. This work shows mainstream 3D model retrieval algorithm programs based on deep learning currently developed remotely, and further subdivides their advantages and disadvantages according to the behavior evaluation of the algorithm programs obtained by trial. According to other restoration applications, the main 3D model retrieval algorithms can be divided into two categories: (1) 3D standard restoration methods supported by the model, i.e., both the restored object and the recalled object are 3D models. It can be further divided into voxel-based, point coloring-based, and appearance-based methods, and (2) cross-domain 3D model recovery methods supported by 2D replicas, that is, the retrieval motivation is 2D images, and the recovery appearance is 3D models, including retrieval methods supported by 2D display, 2D depiction-based realistic replication and 3D mold recovery methods. Finally, the work proposed novel 3D fashion retrieval algorithms supported by deep science that are analyzed and ventilated, and the unaccustomed directions of future development are prospected.
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50

Kumar, Suneel, Manoj Kumar Singh, and Manoj Kumar Mishra. "Improve Content-based Image Retrieval using Deep learning model." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2327/1/012028.

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Abstract The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
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