Video shot boundary detection using hybrid dual tree complex wavelet transform with Walsh Hadamard transform

Mishra, Ravi Shankar

doi:10.1007/s11042-021-11052-2

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…In this section, the performance analysis of proposed model for fade in and fade out transitions from TRECVID dataset is presented. The results obtained by the proposed SBD model is compared with deep belief network‐social ski driver optimization algorithm (DBN‐SSDOA), deep belief network (DBN), recurrent neural network (RNN), deep neural network (DNN), and CNN 28 . The performance for fade in and fade out transitions are evaluated separately and provided in the following subsections.…”

Section: Resultsmentioning

confidence: 99%

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Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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Summary This paper presents a hybrid feature extraction and optimized deep learning model for the effective shot boundary detection (SBD). A cross‐guided bilateral filtering technique is used for pre‐processing. A hybrid fuzzy histogram with dual tree complex wavelet transform (FH‐DTCWT) is developed for the feature extraction to extract the visual features from the pre‐processed frames of the block. Candidate segment selection process is performed to identify the non‐boundary frames from the extracted features to improve the accuracy. Continuity matrix is created using the possible transition frames to verify that the frames are in sequential order without any gap. Classification of transition types are performed by the proposed optimized deep convolutional neural network. Optimized deep learning (DCNN‐RBESO) model is the combination of deep convolutional neural network (DCNN) and Rider bald eagle search optimization (RBESO) algorithm. To update the weight of DCNN model in learning, RBESO is utilized. MATLAB tool is used to develop the proposed SBD model. The experimental analysis is carried out for the proposed model using TREC Video Retrieval Evaluation (TRECVID) and VideoSeg datasets. The proposed model outperformed in the detection of shot boundaries in terms of precision, recall, and F1‐score.

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Section: Resultsmentioning

confidence: 99%

“…Figure 6 shows the performance achieved by the proposed SBD model and the existing techniques in terms of recall, precision and F1-score. 28 The performance for fade in and fade out transitions are evaluated separately and provided in the following subsections.…”

Section: Comparative Analysis For Videoseg Dataset (Cut Transition)mentioning

confidence: 99%

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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“…Various methods have been proposed for detecting scene changes in video image sequences. For example, Bi et al [60] used coherent spatio-temporal patterns, Lu and Shi [61] performed candidate segment selection and Singular Value Decomposition (SVD), Mishra [62] employed a complex dual-tree wavelet with Walsh-Hadamard transform. Rashmi and Nagendraswamy [63] used a block-based cumulative approach.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

Information

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In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

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“…Automated video segmentation algorithms usually emphasize spatiotemporally connected groupings of video pixels and compact (consistent motion and appearance). Motion analysis is one of the early solutions [21], [22], and is based on assessing backgroundinduced motion patterns and geometry constraints. A wide class of models based on trajectory are used for exploiting the long-term motion information.…”

Section: Related Workmentioning

confidence: 99%

“…Hybrid dual tree complex wavelet transform [21] Technique uses Walsh Hadamard transform (DTCWT-WHT) with Hybrid Dual-Tree Complex Wavelet Transform.…”

Section: Sbd-duo [14]mentioning

confidence: 99%

A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation

Usmani¹,

Watada

Jaafar

et al. 2021

IEEE Access

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Video object segmentation's primary goal is to automatically extract the principal object(s) in the foreground from the background in videos. The primary focus of the current deep learning-based models is to learn the discriminative representations in the foreground over motion and appearance in small-term temporal segments. In the video segmentation process, it is difficult to handle various challenges such as deformation, scale variation, motion blur, and occlusion. Furthermore, relocating the segmentation target in the next frame is difficult if it is lost in the current frame during the segmentation process. This work aims at solving the zero-shot video object segmentation issue in a holistic fashion. We take advantage of the inherent correlations between the video frames by incorporating a global co-attention mechanism to overcome the limitations. We propose a novel reinforcement learning framework that provides competent and fast stages for gathering scene context and global correlations. The agent concurrently calculates and adds the responses of co-attention in the joint feature space. To capture the different aspects of the common feature space, the agent can generate multiple co-attention versions. Our framework is trained using pairs (or groups) of video frames, which adds to the training content, thus increasing the learning capacity. Our approach encodes the important information during the segmentation phase by a simultaneous process of various reference frames that are subsequently utilized to predict the persistent and conspicuous objects in the foreground. The proposed method has been validated using four commonly used video entity segmentation datasets: SegTrack V2, DAVIS 2016, CdNet 2014, and the Youtube-Object dataset. On the DAVIS 2016, the results reveal that the proposed results boost the state-of-the-art techniques on the F1 Measure by 4%, SegTrack V2 by a Jaccard Index of 12.03%, and Youtube Object by a Jaccard Index of 13.11%. Meanwhile, our algorithm improves the accuracy by 8%, F1 Measure by 12.25 %, and precision by 14% on the CdNet 2014, thus ranking higher than the current state-of-the-art methods.

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Video shot boundary detection using hybrid dual tree complex wavelet transform with Walsh Hadamard transform

Cited by 10 publications

References 30 publications

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

A Reinforcement Learning Based Adaptive ROI Generation for Video Object Segmentation

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