In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively classifying, managing and retrieving video information has become a difficult problem to solve. In this paper, an improved crow search algorithm is used to process video images, and the information entropy is used to extract the key frames, which reduces the computation burden of each frame feature calculation and feature contrast process, thus shortening the key frame detection time. Then, all the feature sets are extracted and used as input for an HMM according to the observed sequence $$O = O_{1} ,O_{2} ,O_{3} , \cdot \cdot \cdot ,O_{T}$$ of the input image or video data and the initial model parameters $$\lambda = (\pi ,A,B)$$ . According to the training rules, the model parameters are repeatedly adjusted and modified, and the new model $$\overline{\lambda }$$ is constructed step by step to realize the retrieval of multimedia images and videos. The experimental results show that the method has obvious advantages in terms of the retrieval time and retrieval effect and provides new ideas for multimedia image and video retrieval.

中文翻译：

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基于改进HMM的多媒体图像和视频检索

在当今的信息时代，信息是从文本和更复杂的媒体（例如图像、音频和视频）中收集的。在这些数据来源中，视频信息的快速增长使其逐渐成为人们生活中的主要信息来源。视频信息具有信息种类多、形式复杂、结构化程度低的特点。因此，对视频信息进行有效的分类、管理和检索就成为一个亟待解决的难题。本文采用改进的乌鸦搜索算法对视频图像进行处理，利用信息熵提取关键帧，减少了每帧特征计算和特征对比过程的计算负担，从而缩短了关键帧检测时间。 . 然后，根据观察到的序列 $$O = O_{1} ,O_{2} ,O_{3} , \cdot \cdot \cdot ,O_{T}$$ 提取所有特征集并用作 HMM 的输入输入图像或视频数据和初始模型参数 $$\lambda = (\pi ,A,B)$$ 。根据训练规则，反复调整和修改模型参数，逐步构建新模型$$\overline{\lambda }$$，实现多媒体图像和视频的检索。实验结果表明，该方法在检索时间和检索效果方面具有明显优势，为多媒体图像和视频检索提供了新思路。根据训练规则，反复调整和修改模型参数，逐步构建新模型$$\overline{\lambda }$$，实现多媒体图像和视频的检索。实验结果表明，该方法在检索时间和检索效果方面具有明显优势，为多媒体图像和视频检索提供了新思路。根据训练规则，反复调整和修改模型参数，逐步构建新模型$$\overline{\lambda }$$，实现多媒体图像和视频的检索。实验结果表明，该方法在检索时间和检索效果方面具有明显优势，为多媒体图像和视频检索提供了新思路。