基于多种花查询的花卉视频检索

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY Artificial Intelligence in Agriculture Pub Date : 2021-01-18 DOI:10.20944/PREPRINTS202101.0318.V1
Manjunath Aradhya, Jyothi Vk, Sharath Kumar, Guru Ds
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引用次数: 1

摘要

从大量视频数据中搜索、识别和检索感兴趣的视频是一种即时需求。这一要求已被认为是计算机视觉、机器学习和模式识别领域的一个活跃研究领域。花卉视频识别与检索在花卉栽培和园艺领域具有重要意义。本文提出了一种用于花卉视频检索的模型。最初,视频用关键帧表示,关键帧中的花从背景中分割出来。然后,从关键帧的花区域提取特征对模型进行分析。将线性判别分析(LDA)用于判别特征的提取。采用多类支持向量机(MSVM)分类器对查询视频进行分类。实验是在我们自己的相对较大的数据集上进行的,包括7788个视频,从三个不同的设备上捕捉到30种不同的花。一般来说,花视频的检索是通过使用由单一种类的花组成的查询视频来解决的。在这项工作中,我们尝试为一个由不同种类的花组成的查询视频开发一个由相似视频检索组成的系统。
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Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers
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.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
期刊最新文献
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