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Transparent Peer Review By Scholar9

CONTENT-BASED VIDEO SHOT BOUNDARY DETECTION USING CANNY EDGE FEATURES

Abstract

Video Shot Boundary Detection (VSBD) is a vital preliminary step in content-based video analysis, enabling efficient indexing, retrieval, summarization, and editing workflows. This paper introduces a robust and computationally efficient method for detecting shot boundaries especially abrupt cuts using the Canny edge features. Our approach employs the following pipeline: frames are converted to grayscale and Canny edge features are extracted. Normalized ECR is computed on consecutive frames, then threshold is set and shot transitions are flagged. We benchmark our method on standard datasets such as TRECVID, evaluating performance in terms of precision, recall, F1-score. Our proposed method average F1-score 93.17.

Arnab Kar Reviewer

badge Review Request Accepted

Arnab Kar Reviewer

19 Aug 2025 11:29 AM

badge Approved

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

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.

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IJ Publication Publisher

Respected Sir,

Thank you for sharing your detailed assessment of the Research Article on Video Shot Boundary Detection. We value your recognition of the work’s originality, computational efficiency, and clarity in methodology, as well as the strong F1-score achieved on the TRECVID dataset. At the same time, your constructive remarks regarding threshold justification, benchmarking with alternative approaches, and extending analysis to gradual transitions are well-noted and will guide meaningful improvements.

Your balanced feedback on both strengths and limitations is highly appreciated, as it provides clear direction for refinement.

Publisher

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IJ Publication

Reviewer

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Arnab Kar

More Detail

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Paper Category

Electronics & Communication

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Journal Name

JETIR - Journal of Emerging Technologies and Innovative Research

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p-ISSN

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e-ISSN

2349-5162

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