Multimedia image and video retrieval based on an improved HMM
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SPECIAL ISSUE PAPER
Multimedia image and video retrieval based on an improved HMM Yanbing Liu1,2 · Sanjev Dhakal1,2 · Binyao Hao3
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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 = O1 , O2 , O3 , ⋅ ⋅ ⋅, OT of the input image or video data and the initial model parameters 𝜆 = (𝜋, A, B) . According to the training rules, the model parameters are repeatedly adjusted and modified, and the new model 𝜆 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. Keywords HMM · Video retrieval · Information entropy · Crow search algorithm
1 Introduction Image retrieval is one of the basic technologies of computer applications. It consists of the visual representation and classification of images. It mainly uses various descriptors and features that represent visual information such as content, meaning and image structure. In this way, one can find the same or similar information in a large number of image databases and use the item as a query image. In the past few years, with the continuous development of artificial * Yanbing Liu [email protected] Sanjev Dhakal [email protected] Binyao Hao [email protected] 1
Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Chengdu University of Technology, Chengdu 610059, China
intelligence and the Internet, image retrieval has attracted increasing attention from researchers, and neural networks, computer networks and other methods have been developed. Machine learning and other methods have become the backbone of the social service industry and industrial upgrading. Scene segmentation is often called story unit segmentation, and its goal is to obtain the scene with the smallest semantic structural unit in a video. Kumar et al. [1] proposed a secure cloud server-based encrypted medica
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