CONTENT-BASED VIDEO SHOT BOUNDARY DETECTION USING CANNY EDGE FEATURES

Approved

Relevance and Originality

The Research Article addresses Video Shot Boundary Detection (VSBD), a critical task for indexing, summarization, and editing in video analysis. By adopting Canny edge features, it proposes a streamlined and efficient approach compared to heavier learning-based techniques. The focus on abrupt cut detection ensures practical applicability, while the contribution lies in balancing high accuracy with computational efficiency, making it both novel and relevant to the field.

Methodology

The pipeline is described with clarity, consisting of grayscale conversion, edge feature extraction, normalized ECR computation, and thresholding for shot detection. This structured design is easy to reproduce and demonstrates sound technical planning. However, more discussion on the sensitivity of thresholds across diverse video content and the rationale for parameter settings would enhance methodological rigor. Comparisons with other feature-based approaches could also strengthen the evaluation.

Validity & Reliability

The reported performance, with an average F1-score of 93.17 on TRECVID benchmarks, demonstrates reliability and strong empirical support. Inclusion of precision and recall ensures a balanced view of accuracy. Yet, the robustness of the approach for gradual transitions, highly dynamic scenes, or noisy datasets remains underexplored. Acknowledging these limitations would improve confidence in the generalizability of the findings.

Clarity and Structure

The Research Article is organized in a logical sequence, beginning with the importance of VSBD, moving through the proposed method, and ending with evaluation results. The explanations are concise, making technical points accessible to readers. Incorporating illustrative figures, detection samples, or comparative tables would further enhance clarity and reader engagement, while a deeper emphasis on real-world applications could add value.

Result Analysis

The analysis shows that the method achieves competitive results with strong performance metrics, validating the effectiveness of edge-based detection. However, the narrow focus on abrupt cuts limits broader applicability, as gradual transitions are not sufficiently examined.