# Interested Keyframe Extraction of Commodity Video Based on Adaptive Clustering Annotation
## 1 Introduction
Existing keyframe extraction algorithms have achieved good results in motion target detection, key figure detection, creating video abstracts, and other fields but these algorithms either lack static feature extraction or pay attention to the overall features of the video while ignoring the uniqueness of a single frame or needing prior parameters that greatly limits the application value of keyframe extraction. In addition, these studies mainly focused on the movement information in the video and did not consider individual demands. In fact, different users have different concerns when browsing a video, so extracting keyframes only by motion information is difficult to meet user's demands. For instance, on the e-commerce platform [16], the commodity video provides vital information for users searching for their needs, and the key is how the user can obtain the interesting information about the video in a very short time. Such information goes beyond movement changes, as representing global and local content about products is more crucial. Besides, the commodity video is commonly short video and movement of the object is not obvious, so traditional keyframe extraction algorithms based on motion changes no longer apply. At the same time, the definition of keyframe regarding the commodity video varies from person to person. Therefore, how to extract video keyframes according to the needs of different users has become a challenge to the traditional algorithm. In recent years, many keyframe extraction algorithms have focused on adding an attention mechanism. [PERSON] [17] designed an attention model based on semantic and visual information to mark frames, and selected frames with high scores as keyframes. Although the attention mechanism is integrated, there is no solution to deal with different users' interests. Aiming at the above problems, we propose an algorithm of commodity video keyframe recognition based on adaptive clustering annotation to solve commodity video keyframe extraction. First of all, from the perspective of users' demands, the keyframe is defined as the frame containing global and local information that users are interested in, and these frames could not include noise and blur. Then, the frame-to-frame difference method is used to obtain the differential frame set by looking for the maximum difference between frames, where a differential frame is defined as the frame that has the greater difference and object movement compared to adjacent frames. For the set, an efficient and adaptive clustering strategy with few parameters based on frame difference degree and category scale is designed to realize the clustering of different frames and the differential frame sets are divided and annotated based on the keyframe definition defined by users' requirements. Furthermore, a small number of marked commodity keyframe samples are utilized to train the deep neural network by means of transfer learning [18] to realize accurate keyframe recognition and extraction. Contributions of this paper mainly include the following four parts: (1) From the perspective of users' attention and interest in commodities, we propose the definition of commodity video keyframe. (2) An adaptive image clustering strategy based on the frame-to-frame difference and keyframe labeling method are proposed. (3) A deep neural network model is presented fusing the frame-to-frame difference with the transfer mechanism to realize the extraction of commodity video keyframes. (4) The proposed algorithm is applied to the self-built video library of clothing commodities, and the results show its effectiveness in meeting the personalized needs of users. Figure 1 shows the algorithm framework of our work. The paper is organized as follows: In Section 2, we provide a review of related work about keyframe extraction, image clustering, and deep neural network algorithm.