Improved Fuzzy-Based SVM Classification System Using Feature Extraction for Video Indexing and Retrieval

Gayathri, N.; Mahesh, K.

doi:10.1007/s40815-020-00884-z

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…Previously, video retrieval relied primarily on users manually defining keywords for key images in the video and locating the segments they needed. At the moment, the most common video data browsing mode is sequential fast forward and backward [9,10]. A continuous image frame recorded by the camera from opening to closing is referred to as a lens.…”

Section: Introductionmentioning

confidence: 99%

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Chen

2022

Wireless Communications and Mobile Computing

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The development requirements for sports panorama synthesis technology, which rely on modern network technology, abandon traditional basketball training forms, and make effective use of the application and development of video panorama technology in the actual training process, are not only an important response to the current characteristics of students’ physical education learning and physical and mental growth but also an important response to the current characteristics of students’ physical education learning and physical and mental growth. In sports video analysis technology, a sports video panorama is a technical tool that converts an action video into a static action panorama to achieve the effect of action freezing and make overall analysis and mastery of the action easier. Shot segmentation is the foundation of hierarchical video structure, and it necessitates the accurate detection of all types of complex edited shot boundaries, as well as the effective distinction of motion changes in shots, in order to avoid shot boundary recognition being hampered. The synthesis technology for sports video panorama is investigated in this paper using edge computing and video shot boundary detection. After obtaining the boundary feature that describes the video shot, a comparison of this feature with a predetermined threshold value can be used to determine whether there is shot shear.

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Section: Introductionmentioning

confidence: 99%

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Chen

2022

Wireless Communications and Mobile Computing

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“…The Histogram of Oriented Gradients (HOG) feature descriptor is the eighth feature extraction method used in this study. It is related to the Canny Edge Detector and the SIFT, and it is used in image processing to detect objects [44]. The method counts how many times a gradient orientation appears in a specific picture section.…”

Section: Image Data Analysis-feature Extractionmentioning

confidence: 99%

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Rathnayake

Dang

et al. 2022

Sensors

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Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.

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“…The frame number is used to index the frame sequences. After the video is broken, the frames obtained will be the same size 3 . Generally 25–30 pictures are taken every second.…”

Section: Introductionmentioning

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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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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Improved Fuzzy-Based SVM Classification System Using Feature Extraction for Video Indexing and Retrieval

Cited by 13 publications

References 17 publications

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

Sports Video Panorama Synthesis Technology Based on Edge Computing and Video Shot Boundary Detection

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

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

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