{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T11:28:21Z","timestamp":1772018901814,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811611025","type":"print"},{"value":"9789811611032","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-1103-2_19","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T08:03:32Z","timestamp":1616659412000},"page":"214-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["On the Performance of Convolutional Neural Networks Under High and Low Frequency Information"],"prefix":"10.1007","author":[{"given":"Roshan Reddy","family":"Yedla","sequence":"first","affiliation":[]},{"given":"Shiv Ram","family":"Dubey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"issue":"2","key":"19_CR1","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s00371-019-01630-9","volume":"36","author":"A Agrawal","year":"2020","unstructured":"Agrawal, A., Mittal, N.: Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Vis. Comput. 36(2), 405\u2013412 (2020)","journal-title":"Vis. Comput."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Babu, K.K., Dubey, S.R.: PCSGAN: perceptual cyclic-synthesized generative adversarial networks for thermal and NIR to visible image transformation. arXiv preprint arXiv:2002.07082 (2020)","DOI":"10.1016\/j.neucom.2020.06.104"},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2019.10.008","volume":"378","author":"SS Basha","year":"2020","unstructured":"Basha, S.S., Dubey, S.R., Pulabaigari, V., Mukherjee, S.: Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing 378, 112\u2013119 (2020)","journal-title":"Neurocomputing"},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/LSP.2020.2964161","volume":"27","author":"JY Choi","year":"2020","unstructured":"Choi, J.Y., Lee, B.: Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for Alzheimer\u2019s disease classification. IEEE Signal Process. Lett. 27, 206\u2013210 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"4500","DOI":"10.1109\/TNNLS.2019.2955777","volume":"31","author":"SR Dubey","year":"2019","unstructured":"Dubey, S.R., Chakraborty, S., Roy, S.K., Mukherjee, S., Singh, S.K., Chaudhuri, B.B.: DiffGrad: an optimization method for convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 31, 4500\u20134511 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"19_CR6","unstructured":"Dubey, S.R., Roy, S.K., Chakraborty, S., Mukherjee, S., Chaudhuri, B.B.: Local bit-plane decoded convolutional neural network features for biomedical image retrieval. Neural Comput. Appl. 32, 7539\u20137551 (2020)"},{"key":"19_CR7","unstructured":"Hayou, S., Doucet, A., Rousseau, J.: On the selection of initialization and activation function for deep neural networks. arXiv preprint arXiv:1805.08266 (2018)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR10","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"issue":"8","key":"19_CR11","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455\u20135516 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09825-6","journal-title":"Artif. Intell. Rev."},{"key":"19_CR12","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Master\u2019s thesis, University of Tront (2009)"},{"key":"19_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"19_CR14","first-page":"7","volume":"7","author":"Y Le","year":"2015","unstructured":"Le, Y., Yang, X.: Tiny ImageNet visual recognition challenge. CS 231N 7, 7 (2015)","journal-title":"CS 231N"},{"issue":"7553","key":"19_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11\u201326 (2017)","journal-title":"Neurocomputing"},{"issue":"10","key":"19_CR17","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1109\/LSP.2016.2601691","volume":"23","author":"C Ma","year":"2016","unstructured":"Ma, C., Xu, Y., Ni, B., Yang, X.: When correlation filters meet convolutional neural networks for visual tracking. IEEE Signal Process. Lett. 23(10), 1454\u20131458 (2016)","journal-title":"IEEE Signal Process. Lett."},{"key":"19_CR18","unstructured":"Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)"},{"key":"19_CR19","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"19_CR20","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","volume":"17","author":"SK Roy","year":"2019","unstructured":"Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B.: HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 17, 277\u2013281 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"19_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"issue":"3","key":"19_CR22","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1109\/LSP.2017.2657381","volume":"24","author":"J Salamon","year":"2017","unstructured":"Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett. 24(3), 279\u2013283 (2017)","journal-title":"IEEE Signal Process. Lett."},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"19_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Singh, V., Sharma, A., Devanathan, S., Mittal, A.: High-frequency refinement for sharper video super-resolution. In: The IEEE Winter Conference on Applications of Computer Vision, pp. 3299\u20133308 (2020)","DOI":"10.1109\/WACV45572.2020.9093572"},{"issue":"1","key":"19_CR27","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR28","unstructured":"Srivastava, Y., Murali, V., Dubey, S.R.: Hard-mining loss based convolutional neural network for face recognition. arXiv preprint arXiv:1908.09747 (2019)"},{"key":"19_CR29","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-981-15-8697-2_30","volume-title":"Computer Vision, Pattern Recognition, Image Processing, and Graphics","author":"Y Srivastava","year":"2020","unstructured":"Srivastava, Y., Murali, V., Dubey, S.R.: A performance evaluation of loss functions for deep face recognition. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds.) NCVPRIPG 2019. CCIS, vol. 1249, pp. 322\u2013332. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-8697-2_30"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"8","key":"19_CR31","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/LSP.2019.2922498","volume":"26","author":"DB Tariang","year":"2019","unstructured":"Tariang, D.B., Chakraborty, R.S., Naskar, R.: A robust residual dense neural network for countering antiforensic attack on median filtered images. IEEE Signal Process. Lett. 26(8), 1132\u20131136 (2019)","journal-title":"IEEE Signal Process. Lett."},{"issue":"7","key":"19_CR32","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1109\/LSP.2019.2920250","volume":"26","author":"T Tirer","year":"2019","unstructured":"Tirer, T., Giryes, R.: Super-resolution via image-adapted denoising CNNs: incorporating external and internal learning. IEEE Signal Process. Lett. 26(7), 1080\u20131084 (2019)","journal-title":"IEEE Signal Process. Lett."},{"key":"19_CR33","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/LSP.2020.2966888","volume":"27","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J.: A deep learning approach in the discrete cosine transform domain to median filtering forensics. IEEE Signal Process. Lett. 27, 276\u2013280 (2020)","journal-title":"IEEE Signal Process. Lett."},{"issue":"3","key":"19_CR34","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1109\/LSP.2019.2892233","volume":"26","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., Li, Y., Wang, S.: Unsupervised deep hashing with adaptive feature learning for image retrieval. IEEE Signal Process. Lett. 26(3), 395\u2013399 (2019)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1103-2_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:39:40Z","timestamp":1619264380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1103-2_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811611025","9789811611032"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1103-2_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prayagraj","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2020.iiita.ac.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"352","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was partially held in a virtual mode.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}