An Efficient Video Frames Retrieval System Using Speeded Up Robust Features Based Bag of Visual Words
Abstract Most studies in content-based image retrieval (CBIR) systems use database images of multiple classes. There is a lack of an automatic video frame retrieval system based on the query image. Low-level features i.e., the shape and colors of most of the objects are almost the same e.g., the sun and an orange are both round and red in color. Features such as speeded up robust features (SURF) used in most of the content-based video retrieval (CBVR) & CBIR research work are non-invariant features which may affect the overall accuracy of the CBIR system. The use of a simple and weak classifier or matching technique may also affect the accuracy of the CBIR system on high scale. The unavailability of datasets for content-based video frames retrieval is also a research gap to be explored in this paper.
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Anzid, H., le Goic, G., Bekkari, A., Mansouri, A., & Mammass, D. (2023). A new SURF-based algorithm for robust registration of multimodal images data. The Visual Computer, 39(4), 1667-1681. https://doi.org/10.1007/s00371-022-02435-z
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Kavitha, A. R., Simon, M. D., & Sumathy, G. (2023). Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System.
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Mounika, B. R., Palanisamy, P., Sekhar, H. H., & Khare, A. (2023). Content based video retrieval using dynamic textures. Multimedia Tools and Applications, 82(1), 59-90. https://doi.org/10.1007/s11042-022-13086-6
Prathiba, T., & Kumari, R. S. S. (2023). Retraction Note to: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction. J Ambient Intell Human Comput,14 (Suppl 1), 315. https://doi.org/10.1007/s12652-022-04085-4
Prathiba, T., Shantha Selva Kumari, R., & Chengathir Selvi, M. (2023). ALMEGA-VIR: face video retrieval system. The Imaging Science Journal, 1-11.
Rastegar, H., & Giveki, D. (2023). Designing a new deep convolutional neural network for content-based image retrieval with relevance feedback. Computers and Electrical Engineering, 106, 108593.
Salih, F. A. A., & Abdulla, A. A. (2023). Two-layer content-based image retrieval technique for improving effectiveness. Multimedia Tools and Applications, 1-22.
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Sikandar, S., Mahum, R., & Alsalman, A. (2023). A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion. Applied Sciences, 13(7), 4581.
Sowmyayani, S., & Rani, P. A. J. (2023). Content based video retrieval system using two stream convolutional neural network. Multimedia Tools and Applications, 1-19.
Usher, L. E. (2023). The case for reflexivity in quantitative survey research in leisure studies: lessons from surf research. Annals of Leisure Research, 26(2), 269-284.
Veselý, P., & Peška, L. (2023, January). Less Is More: Similarity Models for Content-Based Video Retrieval. In International Conference on Multimedia Modeling (pp. 54-65). Cham: Springer Nature Switzerland.
Victoria Priscilla, C., & Rajeshwari, D. (2023). Performance Analysis of Spatio-temporal Human Detected Keyframe Extraction. Journal of Survey in Fisheries Sciences, 10(2S), 233-243.
Vieira, G. S., Fonseca, A. U., & Soares, F. (2023a). CBIR-ANR: A content-based image retrieval with accuracy noise reduction. Software Impacts, 15, 100486.
Vieira, G., Fonseca, A., Sousa, N., Felix, J., & Soares, F. (2023b). A novel content-based image retrieval system with feature descriptor integration and accuracy noise reduction. Expert Systems with Applications, 120774.
Walter, K. H., Otis, N. P., Miggantz, E. L., Ray, T. N., Glassman, L. H., Beltran, J. L., ... & Michalewicz-Kragh, B. (2023). Psychological and functional outcomes following a randomized controlled trial of surf and hike therapy for US service members. Frontiers in Psychology, 14, 1185774.
Wickstrøm, K. K., Østmo, E. A., Radiya, K., Mikalsen, K. Ø., Kampffmeyer, M. C., & Jenssen, R. (2023). A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Computerized Medical Imaging and Graphics, 107, 102239.
Awasthi, D., & Srivastava, V. K. (2022). Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF. Multimedia Tools And Applications, 82(11), 16555-16589. https://doi.org/10.1007/s11042-022-14002-8
Bhagat, M., & Kumar, D. (2023). Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimedia Tools and Applications, 1-25.
Fan, J., Yang, X., Lu, R., Li, W., & Huang, Y. (2023). Long-term visual tracking algorithm for UAVs based on kernel correlation filtering and SURF features. The Visual Computer, 39(1), 319-333. https://doi.org/10.1007/s00371-021-02331-y
Huang, C., Vasudevan, V., Pastor-Serrano, O., Islam, M. T., Nomura, Y., Dubrowski, P., et al. (2023). Learning image representations for content-based image retrieval of radiotherapy treatment plans. Physics in Medicine & Biology, 68(9), 095025. https://doi.org/10.1088/1361-6560/accdb0
Huo, S., Zhou, Y., Xiang, W., & Kung, S. Y. (2023). Weakly-supervised content-based video moment retrieval using low-rank video representation. Knowledge-Based Systems, 277, 110776. https://doi.org/10.1016/j.knosys.2023.110776
Hussain, A., Ahmad, M., Hussain, T., & Ullah, I. (2022). Efficient content based video retrieval system by applying AlexNet on key frames. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 207-235. https://doi.org/10.14201/adcaij.27430
KA, R., Simon, M. D., & Sumathy, G. (2023). Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System.
Kakizaki, K., Fukuchi, K., & Sakuma, J. (2023). Certified Defense for Content Based Image Retrieval. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4561-4570). https://doi.org/10.1109/WACV56688.2023.00454
Kavitha, A. R., Simon, M. D., & Sumathy, G. (2023). Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System.
Kovač, I., & Marák, P. (2023). Finger vein recognition: utilization of adaptive gabor filters in the enhancement stage combined with sift/surf-based feature extraction. Signal, Image and Video Processing, 17(3), 635-641. https://doi.org/10.1007/s11760-022-02270-8
Megala, G., Swarnalatha, P., Prabu, S., Venkatesan, R., & Kaneswaran, A. (2023). Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach. In P. Swarnalatha & S. Prabu (Eds.), Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT (pp. 18-28). IGI Global. https://doi.org/10.4018/978-1-6684-8098-4.ch002
Mounika, B. R., Palanisamy, P., Sekhar, H. H., & Khare, A. (2023). Content based video retrieval using dynamic textures. Multimedia Tools and Applications, 82(1), 59-90. https://doi.org/10.1007/s11042-022-13086-6
Prathiba, T., & Kumari, R. S. S. (2023). Retraction Note to: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction. J Ambient Intell Human Comput,14 (Suppl 1), 315. https://doi.org/10.1007/s12652-022-04085-4
Prathiba, T., Shantha Selva Kumari, R., & Chengathir Selvi, M. (2023). ALMEGA-VIR: face video retrieval system. The Imaging Science Journal, 1-11.
Rastegar, H., & Giveki, D. (2023). Designing a new deep convolutional neural network for content-based image retrieval with relevance feedback. Computers and Electrical Engineering, 106, 108593.
Salih, F. A. A., & Abdulla, A. A. (2023). Two-layer content-based image retrieval technique for improving effectiveness. Multimedia Tools and Applications, 1-22.
Salih, S. F., & Abdulla, A. A. (2023). An effective bi-layer content-based image retrieval technique. The Journal of Supercomputing, 79(2), 2308-2331.
Sikandar, S., Mahum, R., & Alsalman, A. (2023). A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion. Applied Sciences, 13(7), 4581.
Sowmyayani, S., & Rani, P. A. J. (2023). Content based video retrieval system using two stream convolutional neural network. Multimedia Tools and Applications, 1-19.
Usher, L. E. (2023). The case for reflexivity in quantitative survey research in leisure studies: lessons from surf research. Annals of Leisure Research, 26(2), 269-284.
Veselý, P., & Peška, L. (2023, January). Less Is More: Similarity Models for Content-Based Video Retrieval. In International Conference on Multimedia Modeling (pp. 54-65). Cham: Springer Nature Switzerland.
Victoria Priscilla, C., & Rajeshwari, D. (2023). Performance Analysis of Spatio-temporal Human Detected Keyframe Extraction. Journal of Survey in Fisheries Sciences, 10(2S), 233-243.
Vieira, G. S., Fonseca, A. U., & Soares, F. (2023a). CBIR-ANR: A content-based image retrieval with accuracy noise reduction. Software Impacts, 15, 100486.
Vieira, G., Fonseca, A., Sousa, N., Felix, J., & Soares, F. (2023b). A novel content-based image retrieval system with feature descriptor integration and accuracy noise reduction. Expert Systems with Applications, 120774.
Walter, K. H., Otis, N. P., Miggantz, E. L., Ray, T. N., Glassman, L. H., Beltran, J. L., ... & Michalewicz-Kragh, B. (2023). Psychological and functional outcomes following a randomized controlled trial of surf and hike therapy for US service members. Frontiers in Psychology, 14, 1185774.
Wickstrøm, K. K., Østmo, E. A., Radiya, K., Mikalsen, K. Ø., Kampffmeyer, M. C., & Jenssen, R. (2023). A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Computerized Medical Imaging and Graphics, 107, 102239.
Hussain, A. (2023). An Efficient Video Frames Retrieval System Using Speeded Up Robust Features Based Bag of Visual Words. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e28824. https://doi.org/10.14201/adcaij.28824
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