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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The article begins by highlighting the dynamic nature of image processing in the context of advancing information technology. It predicts a future shift from image to video processing due to the increasing prevalence of videos over text-based media. The Video Stream Matcher (VSM) is introduced as a tool for analyzing video data using statistical models like KS Test, Variation, Mean, and Norm.
Key frames extraction is identified as a technique to minimize the vast collection of frames in a video. Four features are extracted from each frame: histogram, edge, slope, and wavelets. The literature review section references research papers and books that inform the implementation of VSM, including studies on similarity analysis of video sequences and key frame extraction.
The key elements of VSM are outlined, including the input of source and test data (videos or images), key frames extraction, feature extraction, and application of statistical models for decision making. The problem statement highlights the need for automated video evaluation systems, particularly in scenarios like security checks on public transportation and video database management in TV stations.
The proposed solution revolves around VSM’s ability to process videos, focusing on key frames extraction, feature extraction, and statistical model application for decision making. The scope of the project encompasses mean frame extraction, feature extraction algorithms, and dual-phase statistical decision making. The organization of the report is structured to cover research findings, implementation details, software functioning, and conclusions with future work discussions.
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.
Parametric data follows a specific distribution with consistent distances from the mean, while non-parametric data lacks a specific distribution. Mean frame histogram data, being non-parametric, employs the KS Test. Four features are chosen for extraction: Histogram, Edge, Slope, and Wavelets. Histogram feature extraction involves arranging data in columns and addressing the issue of identical mean values in different distributions by weighting the matrix with a column vector. This process ensures unique results, crucial for decision-making, as mean and variance pairs differ, offering valuable insights.
