CONTENT-BASED VIDEO SHOT BOUNDARY DETECTION USING CANNY EDGE FEATURES

Approved

Relevance and Originality

The Research Article focuses on Video Shot Boundary Detection (VSBD), a critical component in video indexing, retrieval, and summarization. By employing Canny edge features, the study contributes an efficient alternative to complex machine learning models, balancing performance with computational cost. The emphasis on abrupt cut detection makes the work particularly relevant for practical video analysis pipelines, offering originality through a lightweight yet effective solution.

Methodology

The proposed pipeline is described in a stepwise manner—grayscale conversion, edge feature extraction, normalized ECR calculation, and threshold-based detection—making the approach transparent and reproducible. While the design is clear, more explanation on parameter selection, particularly the threshold setting, would enhance methodological rigor. Benchmarking against additional algorithms would further strengthen the comparative analysis and highlight the advantages of the chosen design.

Validity & Reliability

With an average F1-score of 93.17 on the TRECVID dataset, the results appear convincing and demonstrate robustness. The inclusion of precision and recall metrics provides a balanced performance evaluation. However, the lack of discussion on diverse scenarios such as gradual transitions or videos with heavy motion reduces confidence in broader generalizability. Addressing these aspects would improve reliability.

Clarity and Structure

The Research Article is presented with clarity, maintaining a logical flow from problem definition to experimental validation. The writing style is concise and accessible, aiding comprehension of technical content. Nevertheless, visual aids such as detection frame samples or comparative charts could enrich understanding and provide clearer evidence of the method’s strengths.

Result Analysis

The results confirm that the proposed approach achieves competitive performance for abrupt cut detection, with evaluation metrics supporting the conclusions. Yet, the focus on abrupt boundaries without addressing gradual transitions limits the overall scope of the contribution.