Article
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Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers
Version 1
: Received: 15 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (11:29:59 CET)
How to cite: Aradhya, M.; VK, J.; Kumar, S.; DS, G. Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers. Preprints 2021, 2021010318 (doi: 10.20944/preprints202101.0318.v1). Aradhya, M.; VK, J.; Kumar, S.; DS, G. Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers. Preprints 2021, 2021010318 (doi: 10.20944/preprints202101.0318.v1).
Abstract
Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.
Subject Areas
Flower Region of Interest (FRoI); Linear Discriminant Analysis (LDA); retrieval of flower videos; Multiclass Support Vector Machine
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.
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