Over the past 20+ years, I have been working as a software engineer, architect, and programmer, creating, designing, and programming various applications. My main focus has always been to achieve business goals and transform business ideas into digital reality. I have successfully solved numerous business problems and increased productivity for small businesses as well as enterprise corporations through the solutions that I created.
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Algorithm 1, focusing on mean frame extraction from AVI videos, operates similarly for both source and target videos. It stores the first and last frames and extracts a defined percentage of frames (10%) as temporary frames. Each temporary frame undergoes histogram calculation and comparison using the KS Test. Frames with differing distributions are saved as mean frames. This process continues until all frames are processed. The algorithm is outlined with steps for video input, temporary frame extraction, histogram calculation, and mean frame storage, ensuring efficient mean frame extraction for subsequent analysis in video stream matching.
This segment of VSM encompasses two distinct components:
1. Target Image – Source Video – Model
2. Target Video – Source Video – Model
Each component represents a different scenario for comparison: matching a target image with a source video and matching a target video with a source video. These models serve as the foundation for analyzing and comparing video streams, enabling various applications in multimedia processing and content-based similarity analysis.
The VSM architecture comprises three main components: loading target videos, loading source videos, and loading target images into the classification model. Users can load target videos, source videos, or target images for classification purposes. Each component serves a specific function within the system, facilitating the comparison and analysis of video streams. The architecture is designed to handle various types of input data, ensuring flexibility and adaptability for different applications requiring video stream matching.
The study delves into video sequence comparison, essential in multimedia information systems. It explores content-based similarity analysis by extracting key frames from video shots and assessing their similarities. Features like histograms, slopes, edges, and wavelets are extracted and utilized individually or in combinations for similarity matching. Statistical models such as NORM, MEAN, VARIANCE, and KS-TEST are employed to quantify relationships between features in target and query video shots. This approach facilitates robust similarity analysis, crucial for various applications in multimedia processing and information retrieval.
The GUI of the Video Stream Matching (VSM) system includes buttons for loading target images and source videos, displaying them in the respective axes. Users can calculate wavelet, histogram, edge, and slope features for both source videos and target images. Additionally, buttons for different algorithms initiate the decision-making process, activating the statistical model. Results are displayed in the output window after completion of the process. The system provides comprehensive functionality for analyzing and comparing videos and images.
