# Interested Keyframe Extraction of Commodity Video Based on Adaptive Clustering Annotation
## 2 Related Work  
Keyframe extraction has become a hotspot in video processing technology nowadays and mainly uses clustering strategies, such as K-means clustering [19], mean-Shift clustering, density clustering [20], fuzzy C-mean clustering [21], etc. to generate video  
Figure 1: Proposed algorithm framework. summaries and retrieve information. Generally, in the field of video retrieval, the video is usually transformed into keyframes so as to improve the efficiency. [PERSON] et al. [22] proposed a keyframe extraction algorithm based on pixel features, which improved the retrieval performance compared with the traditional algorithm based on histogram feature. [PERSON] et al. [21] proposed a keyframe extraction algorithm based on improved fuzzy C-means clustering, extracting frame images from each class as keyframes according to the maximum entropy. [PERSON] et al. [23] dynamically divided frames into different classes in accordance with the captured content and selected the frame closest to the class center as the keyframe. [PERSON] and [PERSON] [24] extracted a moving target from the original video as keyframes through lens boundary detection. [PERSON] et al. [25] used posture information to identify and select keyframes with abrupt posture changes on the basis of human targets, in order to make the network have adaptive ability in attitude changes and, thus, extract keyframes having motion information. Normally, in the field of generating video summary, the extracted keyframe is used as the video summary. [PERSON] et al. [26] divided the video into different segments according to lenses; then, they clustered different video segments, and finally, selected several frames from every category as keyframes of the video. In general, image features are usually fused into the keyframe extraction algorithm based on clustering. For instance, [PERSON] et al. [10] extracted SURF features from video frames and then clustered features to extract keyframes. Likewise, [PERSON] et al. [27] extracted and clustered color features, and sequentially selected the center of each category as keyframes of the video. [PERSON] et al. [28] designed feature description windows to extract the objects and reduced the number of windows through prior knowledge to reduce the possibility of overfitting. [PERSON] et al. [29] considered multiple target detection; they extracted the features of different targets through supervised learning and fused multiple features to extract the keyframe. [PERSON] et al. [11] extracted a frame per second to greatly shorten the length of the video, thus improving the efficiency of the algorithm. With the rapid development of deep learning in the field of video processing, some scholars applied it to extract and recognize keyframe feature. [PERSON] et al. [30] used Recurrent Neural Networks (RNN) to extract video keyframes, which make the keyframe sequence represent the semantic information about the original video better. [PERSON] et al. [31] extracted video features by 3D-CNN and ResNet and then recognized keyframes using a Long Short-Term Memory (LSTM) network trained by these features. Universally, RNN and LSTM are used to process time series data. Although video is a kind of time series data, for commodity video keyframe extraction, users do not focus on the temporal characteristics between two frames and pay more attention to the contents of each image. Therefore, we should consider other neural network models to extract image features that users are interested in to improve the accuracy of keyframe extraction. At present, the convolutional neural network is mainly used to extract image features. Since AlexNet [32] made a breakthrough, the architecture of convolutional neural network (CNN) has been getting deeper and deeper. For example, VGG [33] network and GoogleNet [34] have reached 19 and 22 layers, respectively.