CONTENT-BASED VIDEO SHOT BOUNDARY DETECTION USING CANNY EDGE FEATURES

Approved

Relevance and Originality

The Research Article investigates Video Shot Boundary Detection (VSBD), a core element in video indexing, retrieval, and summarization workflows. By using Canny edge features as the foundation, the study presents a lightweight yet effective approach that offers novelty when compared with resource-intensive deep learning models. The emphasis on abrupt cut detection demonstrates practical significance, showing how efficiency and accuracy can be balanced in content-based video analysis.

Methodology

The proposed design follows a straightforward pipeline involving grayscale conversion, edge feature extraction, normalized ECR calculation, and threshold-based decision making. The structure is well explained and ensures reproducibility. Still, more detail on how threshold values were chosen and whether the method adapts effectively to different video categories would make the methodology stronger. Comparisons with other established approaches could provide deeper validation of the design choices.

Validity & Reliability

The reported F1-score of 93.17 on TRECVID datasets indicates strong performance, supported by precision and recall to show balanced accuracy. This adds credibility to the findings. However, the absence of explicit discussion on handling challenging scenarios such as gradual transitions, high-motion sequences, or noisy video streams leaves questions about broader applicability and generalization of the results.

Clarity and Structure

The Research Article is organized clearly, presenting the problem, the proposed solution, and the evaluation results in a logical sequence. The explanations are concise and technically accessible, allowing readers to follow the reasoning easily. The inclusion of additional visual evidence such as boundary detection examples or comparative plots would make the presentation more engaging and strengthen clarity.

Result Analysis

The performance results highlight the strength of the proposed approach, showing it to be both accurate and computationally efficient for abrupt cut detection. However, the analysis does not extend to gradual boundaries, limiting the comprehensiveness of the conclusions.