{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:19:14Z","timestamp":1784391554726,"version":"3.55.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10586-024-04731-w","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T19:04:57Z","timestamp":1730747097000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An efficient content based image retrieval framework using separable CNNs"],"prefix":"10.1007","volume":"28","author":[{"given":"Sunita","family":"Rani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geeta","family":"Kasana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shalini","family":"Batra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"4731_CR1","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10791-007-9039-3","volume":"11","author":"T Deselaers","year":"2008","unstructured":"Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retr. 11, 77\u2013107 (2008). https:\/\/doi.org\/10.1007\/s10791-007-9039-3","journal-title":"Inf. Retr."},{"key":"4731_CR2","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 91\u2013110 (2004)","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"4731_CR3","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume":"2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. IEEE Conf. Comput. Vis. Pattern Recogn.(CVPR) 2016, 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","journal-title":"IEEE Conf. Comput. Vis. Pattern Recogn.(CVPR)"},{"key":"4731_CR4","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014). https:\/\/api.semanticscholar.org\/CorpusID:14124313"},{"key":"4731_CR5","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. CoRR abs\/1610.02357 (2016). arXiv:1610.02357"},{"key":"4731_CR6","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.\u00a0C., Fei-Fei, L.: Imagenet large scale visual recognition challenge (2014). arXiv:1409.0575","DOI":"10.1007\/s11263-015-0816-y"},{"key":"4731_CR7","doi-asserted-by":"publisher","first-page":"1615","DOI":"10.1007\/s10462-019-09715-6","volume":"53","author":"SDMKKS Arun","year":"2020","unstructured":"Arun, S.D.M.K.K.S., Govindan, V.K.: Enhanced bag of visual words representations for content based image retrieval: a comparative study. Artif. Intell. Rev. 53, 1615\u20131653 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"4731_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107952","volume":"116","author":"QW Jian Zhang","year":"2021","unstructured":"Jian Zhang, Q.W., Cao, Y.: Vector of locally and adaptively aggregated descriptors for image feature representation. Pattern Recogn. 116, 107952 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.107952","journal-title":"Pattern Recogn."},{"key":"4731_CR9","doi-asserted-by":"publisher","unstructured":"Abdel-Nabi, H., Al-Naymat, G., Awajan, A.: Content based image retrieval approach using deep learning, in: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pp. 1\u20138 (2019). https:\/\/doi.org\/10.1109\/ICTCS.2019.8923042","DOI":"10.1109\/ICTCS.2019.8923042"},{"key":"4731_CR10","doi-asserted-by":"publisher","unstructured":"Sikandar, S., Mahum, R., Alsalman, A.: A novel hybrid approach for a content-based image retrieval using feature fusion, Appl. Sci. 13 (7) (2023). https:\/\/doi.org\/10.3390\/app13074581. https:\/\/www.mdpi.com\/2076-3417\/13\/7\/4581","DOI":"10.3390\/app13074581"},{"issue":"3","key":"4731_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3470568","volume":"2","author":"S Maji","year":"2021","unstructured":"Maji, S., Bose, S.: CBIR using features derived by deep learning. ACM\/IMS Trans. Data Sci. 2(3), 1\u201324 (2021). https:\/\/doi.org\/10.1145\/3470568","journal-title":"ACM\/IMS Trans. Data Sci."},{"key":"4731_CR12","unstructured":"Alappat, A.\u00a0L., Nakhate, P., Suman, S., Chandurkar, A., Pimpalkhute, V., Jain, T.: Cbir using pre-trained neural networks (2021). arXiv:2110.14455"},{"key":"4731_CR13","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.14569\/IJACSA.2021.0120723","volume":"12","author":"A Ahmed","year":"2021","unstructured":"Ahmed, A.: Pre-trained CNNs models for content based image retrieval. Int. J. Adv. Comput. Sci. Appl. 12, 2021 (2021). https:\/\/doi.org\/10.14569\/IJACSA.2021.0120723","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"4731_CR14","doi-asserted-by":"crossref","unstructured":"Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval (2014). arXiv:1404.1777","DOI":"10.1007\/978-3-319-10590-1_38"},{"key":"4731_CR15","unstructured":"Chen, W., Liu, Y., Wang, W., Bakker, E., Georgiou, T., Fieguth, P., Liu, L., Lew, M.: Deep image retrieval: A survey. 2101.11282 (2021) 1\u201321. https:\/\/arxiv.org\/abs\/2101.11282"},{"issue":"1","key":"4731_CR16","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cviu.2005.09.012","volume":"106","author":"L Fei-Fei","year":"2007","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Understand. 106(1), 59\u201370 (2007)","journal-title":"Comput. Vis. Image Understanding"},{"key":"4731_CR17","unstructured":"Team, T.: Flower photos dataset. Licensed under the creative commons by-attribution license (CC BY 2.0) (2019). https:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"key":"4731_CR18","unstructured":"Iwana, B.\u00a0K., Raza\u00a0Rizvi, S.\u00a0T., Ahmed, S., Dengel, A., Uchida, S.: Judging a book by its cover. arXiv preprint arXiv:1610.09204 (2016)"},{"issue":"8","key":"4731_CR19","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":"4731_CR20","doi-asserted-by":"crossref","unstructured":"Razavian, A.\u00a0S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. CoRR abs\/1403.6382 (2014). arXiv:1403.6382","DOI":"10.1109\/CVPRW.2014.131"},{"issue":"5","key":"4731_CR21","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1109\/TCSVT.2021.3080920","volume":"32","author":"SR Dubey","year":"2022","unstructured":"Dubey, S.R.: A decade survey of content based image retrieval using deep learning. IEEE Trans. Circ. Syst. Video Technol. 32(5), 2687\u20132704 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3080920","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"4731_CR22","doi-asserted-by":"publisher","unstructured":"Wan, J., Wang, D., Hoi, S.\u00a0C.\u00a0H., Wu, P., Zhu, J., Zhang, Y., Li, J.: Deep learning for content-based image retrieval: A comprehensive study, in: Proceedings of the 22nd ACM international conference on multimedia, MM \u201914. Association for Computing Machinery, New York, pp. 157\u2013166 (2014). https:\/\/doi.org\/10.1145\/2647868.2654948","DOI":"10.1145\/2647868.2654948"},{"key":"4731_CR23","doi-asserted-by":"publisher","unstructured":"Abdel-Nabi, H., Al-Naymat, G., Awajan, A.: Content based image retrieval approach using deep learning, in: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pp. 1\u20138 (2019). https:\/\/doi.org\/10.1109\/ICTCS.2019.8923042","DOI":"10.1109\/ICTCS.2019.8923042"},{"key":"4731_CR24","doi-asserted-by":"publisher","DOI":"10.1145\/3065386","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"4731_CR25","doi-asserted-by":"crossref","unstructured":"Gao\u00a0Huang, Z.\u00a0L., van\u00a0der Maaten, L., Weinberger, K.\u00a0Q.: Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"4731_CR26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231","volume-title":"Inception-v4, inception-ResNet and the impact of residual connections on learning","author":"C Szegedy","year":"2017","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. AAAI Press (2017)"},{"key":"4731_CR27","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4731_CR28","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.\u00a0V.: Learning transferable architectures for scalable image recognition (2018). arXiv:1707.07012","DOI":"10.1109\/CVPR.2018.00907"},{"key":"4731_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Sergey\u00a0Ioffe, J.\u00a0S., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"4731_CR30","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2327\/1\/012028","volume":"2327","author":"S Kumar","year":"2022","unstructured":"Kumar, S., Singh, M.K., Mishra, M.K.: Improve content-based image retrieval using deep learning model. J. Phys.: Conf. Ser. 2327(1), 012028 (2022). https:\/\/doi.org\/10.1088\/1742-6596\/2327\/1\/012028","journal-title":"J. Phys.: Conf. Ser."},{"key":"4731_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-022-04748-1","author":"S Salih","year":"2022","unstructured":"Salih, S., Abdulla, A.: An effective bi-layer content-based image retrieval technique. J. Supercomput. (2022). https:\/\/doi.org\/10.1007\/s11227-022-04748-1","journal-title":"J. Supercomput."},{"key":"4731_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14678-6","author":"F Salih","year":"2023","unstructured":"Salih, F., Abdulla, A.: Two-layer content-based image retrieval technique for improving effectiveness. Multimedia Tools Appl. (2023). https:\/\/doi.org\/10.1007\/s11042-023-14678-6","journal-title":"Multimedia Tools Appl."},{"key":"4731_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-03104-5","author":"V Mahalle","year":"2023","unstructured":"Mahalle, V., Kandoi, N., Patil, S.: A powerful method for interactive content-based image retrieval by variable compressed convolutional info neural networks. Visual Comput (2023). https:\/\/doi.org\/10.1007\/s00371-023-03104-5","journal-title":"Visual Comput"},{"issue":"3","key":"4731_CR34","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/chips2030010","volume":"2","author":"J-J Shang","year":"2023","unstructured":"Shang, J.-J., Phipps, N., Wey, I.-C., Teo, T.H.: A-DSCNN: depthwise separable convolutional neural network inference chip design using an approximate multiplier. Chips 2(3), 159\u2013172 (2023). https:\/\/doi.org\/10.3390\/chips2030010","journal-title":"Chips"},{"key":"4731_CR35","unstructured":"Tan, M., Le, Q.\u00a0V.: Efficientnet: rethinking model scaling for convolutional neural networks. CoRR abs\/1905.11946 (2019). arXiv:1905.11946"},{"key":"4731_CR36","first-page":"350","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Efficient convolution algorithms. In: Goodfellow, I., Bengio, Y., Courville, A. (eds.) Deep learning, 1st edn., pp. 350\u2013354. MIT Press (2016)","edition":"1"},{"key":"4731_CR37","unstructured":"Hua, B., Tran, M., Yeung, S.: Point-wise convolutional neural network. CoRR abs\/1712.05245 (2017). arXiv:1712.05245"},{"key":"4731_CR38","unstructured":"Howard, A.\u00a0G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR abs\/1704.04861 (2017). arXiv:1704.04861"},{"key":"4731_CR39","unstructured":"Sarkar, D.: A comprehensive hands-on guide to transfer learning with real-world applications in deep learning, deep learning on steroids with the power of knowledge transfer! (2018). https:\/\/towardsdatascience.com\/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a"},{"issue":"11","key":"4731_CR40","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227\u20132240 (2014). https:\/\/doi.org\/10.1109\/TPAMI.2014.2321376","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4731_CR41","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1504\/IJICBM.2016.074482","volume":"12","author":"M Arora","year":"2016","unstructured":"Arora, M., Kanjilal, U., Varshney, D.: Evaluation of information retrieval: precision and recall. Int. J. Indian Cult. Bus. Manag. 12, 224 (2016). https:\/\/doi.org\/10.1504\/IJICBM.2016.074482","journal-title":"Int. J. Indian Cult. Bus. Manag."},{"issue":"3","key":"4731_CR42","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1175\/1520-0493(2001)129<0540:EDAASM>2.0.CO;2","volume":"129","author":"KL Elmore","year":"2001","unstructured":"Elmore, K.L., Richman, M.B.: Euclidean distance as a similarity metric for principal component analysis. Monthly Weather Rev 129(3), 540\u2013549 (2001)","journal-title":"Monthly Weather Rev"},{"key":"4731_CR43","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06087-3","author":"A Ouni","year":"2021","unstructured":"Ouni, A., Royer, E., Chevaldonn\u00e9, M., Dhome, M.: Leveraging semantic segmentation for hybrid image retrieval methods. Neural Comput. Appl. (2021). https:\/\/doi.org\/10.1007\/s00521-021-06087-3","journal-title":"Neural Comput. Appl."},{"key":"4731_CR44","unstructured":"Sharif, U., Mehmood, Z., Mahmood, T., Javid, M.\u00a0A., Rehman, A., Saba, T.: Scene analysis and search using local features and support vector machine for effective content-based image retrieval. Artif. Intell. Rev. 1\u201325 (2018). https:\/\/api.semanticscholar.org\/CorpusID:64594474"},{"issue":"37\u201338","key":"4731_CR45","doi-asserted-by":"publisher","first-page":"26995","DOI":"10.1007\/s11042-020-09292-9","volume":"79","author":"L Putzu","year":"2020","unstructured":"Putzu, L., Piras, L., Giacinto, G.: Convolutional neural networks for relevance feedback in content based image retrieval: a content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback. Multimedia Tools Appl. 79(37\u201338), 26995\u201327021 (2020). https:\/\/doi.org\/10.1007\/s11042-020-09292-9","journal-title":"Multimedia Tools Appl."},{"key":"4731_CR46","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1155\/2018\/2134395","volume":"2018","author":"M Yousuf","year":"2018","unstructured":"Yousuf, M., Mehmood, Z., Habib, H.A., Mahmood, T., Saba, T., Rehman, A., Rashid, M.: A novel technique based on visual words fusion analysis of sparse features for effective content-based image retrieval. Math. Probl. Eng. 2018, 13 (2018). https:\/\/doi.org\/10.1155\/2018\/2134395","journal-title":"Math. Probl. Eng."},{"key":"4731_CR47","unstructured":"Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007). https:\/\/api.semanticscholar.org\/CorpusID:118828957"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04731-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04731-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04731-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T15:15:54Z","timestamp":1736522154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04731-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["4731"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04731-w","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"19 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"56"}}