SVM-Based Shot Boundary Detection with a Novel Feature

Matsumoto, Kazunori; Naito, Masaki; Hoashi, Keiichiro; Sugaya, Fumiaki

doi:10.1109/icme.2006.262911

Cited by 21 publications

(14 citation statements)

References 4 publications

(1 reference statement)

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“…. The researches have also proposed many ways of using these features, which range from the simplest approaches, based on thresholding one feature, to more complex approaches, such as adaptive thresholding [3,6], statistical modelling [4], combining several features using rules [7,8] and machine learning techniques [9]. This intense research in the field and the great results that have been achieved have led to some authors to deem the SBD problem as almost resolved [10].…”

Section: Introductionmentioning

confidence: 99%

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Ares

Barreiro

2009

Computer Aided Systems Theory - EUROCAST 2009

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Abstract. Achieving high effectiveness in Shot Boundary Detection (SBD) paves the way for high-level analysis of the video (keyframe extraction, story segmentation, etc.), which makes this step very important. Thus, the SBD problem has been extensively addressed, and many approaches have been proposed. As these approaches have their own different strengths and weaknesses, merging the outcomes of different detectors in order to obtain a better detector comes naturally. In this paper we propose an approach to SBD which takes into account the outcomes of two shot boundary detectors, using a rank fusion technique. This new detector is tested with videos from the TRECVid initiative, finding that it outperforms the two original methods. Moreover, the computation of this merging method is very fast, so it is very attractive for an operational environment.

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Section: Introductionmentioning

confidence: 99%

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Ares

Barreiro

2009

Computer Aided Systems Theory - EUROCAST 2009

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“…As can be seen, the shot boundary detection data has been well curated. Researchers at KDDI R&D Laboratories were responsible for frame-based features [11]. We have used their data to compute feature differences across known shot boundaries for our positive +1 and feature differences at points where shot boundaries are known to be absent for our negative -1 class.…”

Section: Testing Setmentioning

confidence: 99%

Fisher Discrimination with Kernels

Murrell¹,

Hashimoto²,

Takatori³

2011

TMJ

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Fisher first introduced the Fisher linear discriminant back in 1938. After the popularization of the support vector machine (SVM) and the kernel trick it became inevitable that the Fisher linear discriminant would be kernelized. Sebastian Mika accomplished this task as part of his Ph.D. in 2002 and the kernelized Fisher discriminant (KFD) now forms part of the largescale machine-learning tool Shogun. In this article we introduce the package MathKFD. We apply MathKFD to synthetic datasets to demonstrate nonlinear classification via kernels. We also test performance on datasets from the machine-learning literature. The construction of MathKFD follows closely in style the construction of MathSVM by Nilsson and colleagues. We hope these two packages and others of the same ilk will eventually be integrated to form a kernel-based machine-learning environment for Mathematica. ‡ Introduction A two-class machine-learning problem requires learning how to discriminate between data points x i in sample space X belonging to classes y i oe 8+1, -1<, when given only a set of examples 8x i , y i < from each class. The currently popular support vector machine [1] solves this problem through the construction of a hyperplane w.x + b that separates the data points x i , in the sense that all the x i of a given class are on the same side of the plane. In SVMs the separating plane is chosen to maximize the distances from it to the closest data points.The original multidimensional machine-learning algorithm [2] solves the same problem by maximizing between-class to within-class scatter ratio. In this article we describe Fisher's technique and how the introduction of a kernel allows nonlinear classifiers. We build a Kernelized Fisher Discriminant package, MathKFD, and explore its classification capabilities using synthetic and real datasets.The Mathematica Journal 13

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“…Finally, the shot boundaries between dissimilar frames are detected. The shot boundary detection methods can be classified into two approaches: 1) statistical learning-based, such as support vector machine (SVM) (Matsumoto et al, 2006), Adaboost, k nearest neighbor (kNN), Hidden Markov Models (HMM), and clustering algorithms such as K-means and fuzzy K-means (Damnjanovic et al, 2007) 2) threshold-based approaches which detect the boundaries by comparing the measured pair-wis a predefined threshold (Cernekova et al, 2006; Weiming e similarities between frames with et al, 2011).…”

Section: Shot Boundaries Are Fundamental Units Of Videosmentioning

confidence: 99%

Content Based Video Retrieval Based on HDWT and Sparse Representation

Mohamadzadeh

Farsi

2016

Image Anal Stereol

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Video retrieval has recently attracted a lot of research attention due to the exponential growth of video datasets and the internet. Content based video retrieval (CBVR) systems are very useful for a wide range of applications with several type of data such as visual, audio and metadata. In this paper, we are only using the visual information from the video. Shot boundary detection, key frame extraction, and video retrieval are three important parts of CBVR systems. In this paper, we have modified and proposed new methods for the three important parts of our CBVR system. Meanwhile, the local and global color, texture, and motion features of the video are extracted as features of key frames. To evaluate the applicability of the proposed technique against various methods, the P(1) metric and the CC_WEB_VIDEO dataset are used. The experimental results show that the proposed method provides better performance and less processing time compared to the other methods.Keywords: content based video retrieval (CBVR), Hadamard matrix and discrete wavelet transform (HDWT), key frame extraction, shot boundary detection, sparse representation.

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SVM-Based Shot Boundary Detection with a Novel Feature

Cited by 21 publications

References 4 publications

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Fisher Discrimination with Kernels

Content Based Video Retrieval Based on HDWT and Sparse Representation

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