## 1 Introduction  
With videos becoming more popular, important, and pervasive, video tasks, such as searching, retrieving, tracking, summarization, object detection, and copy detection, are becoming more challenging. Video searching (VS) has been a challenging research topic since the mid-1990s, and video copy detection (VCD) also started at that time [1]. Video fingerprinting is widely employed in VS and VCD. The tendency of VCD has been focused on the extraction of robust fingerprints. The state-of-the-art VCD methods are mostly based on video sequences, and image-query-based VS/VCD technology is still imperfect. Therefore, developing robust fingerprints for VS/VCD by using image queries has great importance. Video content analysis is the most basic process for different video applications. Table 1 lists some common video features. It describes the characteristics of those features and lists their robustness and frailty for video fingerprint detection. Most of the features are visual content features, such as color-based, gradient-based, and transform-coefficients-based features, that are global features, and the common features of them are their low complexity extraction and that they are weak under local operations. Local features are local descriptors, such as SIFT (Scale-Invariant Feature Transform) [2], SURF (Speed Up Robust Features) [3], and ORB (Oriented FAST and Rotated BRIEF) [4]. Local features can search for abrupt changes in intensity values and their relationships from their neighboring pixels. Motion-based features represent the temporal relations in video sequences, but in videos that have a few camera activity changes, they cannot represent high-level semantic information better than other features. These features can be used in many applications, such as recognition, retrieval, indexing, identification, searching, tracking, and filtering. For video, they can be applied for keyframe selection, shot boundary detection, video retrieval, identification, and so on. The state-of-the-art VS and VCD methods are mainly based on shot boundary detection (SBD) approaches because using SBD can process video more efficiently. The situation is that image-query-based VCD and deep-learning network-based SBD technologies have yet to be improved. Therefore, in this paper, we aim at developing a video searching/fingerprint detection system based on deep-learning networks and image queries. Figure 1 shows the overview of the image-query-based video searching and copy detection method. The first step is to build a new places-centric dataset and train a pre-trained PlaceNet for image classification. The places dataset should include both natural and computer-generated places-centric images because in many science fiction films the places are not common. The second step is to segment the videos into shots. The SBD method combines general, local, and deep-learning-based features. Then, shot-based fingerprints are built instead of using all frames or keyframes. Finally, several signal processing and geometric transitions are applied to build image queries for video searching and copy detection. Through these steps, the question of whether the image has been found in the video dataset is assessed and if it has, the location of it in the video is detected. Our contributions include: