Version 1
: Received: 29 November 2023 / Approved: 29 November 2023 / Online: 29 November 2023 (16:41:55 CET)
How to cite:
Aradhya, M.; V K, J.; Kumar, S.; D S, G.; Kumar, V. DeepFlower: Archival and Retrieval of Videos. Preprints2023, 2023111909. https://doi.org/10.20944/preprints202311.1909.v1
Aradhya, M.; V K, J.; Kumar, S.; D S, G.; Kumar, V. DeepFlower: Archival and Retrieval of Videos. Preprints 2023, 2023111909. https://doi.org/10.20944/preprints202311.1909.v1
Aradhya, M.; V K, J.; Kumar, S.; D S, G.; Kumar, V. DeepFlower: Archival and Retrieval of Videos. Preprints2023, 2023111909. https://doi.org/10.20944/preprints202311.1909.v1
APA Style
Aradhya, M., V K, J., Kumar, S., D S, G., & Kumar, V. (2023). DeepFlower: Archival and Retrieval of Videos. Preprints. https://doi.org/10.20944/preprints202311.1909.v1
Chicago/Turabian Style
Aradhya, M., Guru D S and Vinay Kumar. 2023 "DeepFlower: Archival and Retrieval of Videos" Preprints. https://doi.org/10.20944/preprints202311.1909.v1
Abstract
This paper presents a model for archival and retrieval of the videos of natural flowers. To design an efficient video retrieval system the stages namely, keyframe selection, feature extraction, feature dimensionality reduction and indexing are essential for fast browsing and accessing of videos. Three different keyframe selection approaches are proposed using clustering algorithms after segmenting flower regions from its background. Deep Convolutional Neural Network is used as a feature extractor. After keyframe selection, a video is represented with a set of keyframes. To reduce the feature dimension of a video, two feature selection methods are utilized. For an efficient archival and fast retrieval of flower videos an indexing method called KD-tree is recommended. For a given query video, similar videos are retrieved both in relative and absolute search modalities. An extensive experimentation conducted on a relatively large flower video dataset. The data set consists of 7788 videos of 30 different species of flowers. The videos are captured with three different devices in different resolutions. The comparative study reveals proposed keyframe selection approaches gives better results. It has also been observed that the videos retrieved in absolute approach with features selected from Binormal separation metric and indexing gives good results.
Keywords
Keyframe selection; Dimensionality Reduction; ReliefF; Bi-normal separation; Indexing; KD-tree; retrieval of flower video; Deep Flower
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
