Single-class SVM for dynamic scene modeling

Junejo, Imran N.; Bhutta, Adeel A.; Foroosh, Hassan

doi:10.1007/s11760-011-0230-z

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…AdaBoost.M2 model is trained with 5000 number of learning cycles and an MLPTPL feature set (Table 3). Dataset2 and Dataset3 give less accuracy than Dataset1 because of over fitting [34]. Over fitting generally occurs because of complex model and too many parameters relative to number of samples.…”

Section: Methodsmentioning

confidence: 99%

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Ratanpara

Patel²

2015

J AUDIO SPEECH MUSIC PROC.

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Singer identification is a difficult topic in music information retrieval because background instrumental music is included with singing voice which reduces performance of a system. One of the main disadvantages of the existing system is vocals and instrumental are separated manually and only vocals are used to build training model. The research presented in this paper automatically recognize a singer without separating instrumental and singing sounds using audio features like timbre coefficients, pitch class, mel frequency cepstral coefficients (MFCC), linear predictive coding (LPC) coefficients, and loudness of an audio signal from Indian video songs (IVS). Initially, various IVS of distinct playback singers (PS) are collected. After that, 53 audio features (12 dimensional timbre audio feature vectors, 12 pitch classes, 13 MFCC coefficients, 13 LPC coefficients, and 3 loudness feature vector of an audio signal) are extracted from each segment. Dimension of extracted audio features is reduced using principal component analysis (PCA) method. Playback singer model (PSM) is trained using multiclass classification algorithms like back propagation, AdaBoost.M2, k-nearest neighbor (KNN) algorithm, naïve Bayes classifier (NBC), and Gaussian mixture model (GMM). The proposed approach is tested on various combinations of dataset and different combinations of audio feature vectors with various Indian male and female PS's songs.

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Section: Methodsmentioning

confidence: 99%

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Ratanpara

Patel²

2015

J AUDIO SPEECH MUSIC PROC.

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“…Other work in [21] proposes the use of the 2D+T curvelet transform for characterisation of dynamic textures in image sequences. In [30], the one-class SVM is proposed for DT recognition purposes resulting in relatively robust features. Other work in [11] proposes a local ternary pattern for background modelling for saliency detection in video sequences.…”

Section: Related Workmentioning

confidence: 99%

Sparse binarised statistical dynamic features for spatio-temporal texture analysis

Arashloo

2018

SIViP

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The paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture. The BSDF descriptor operates by applying three-dimensional spatiotemporal filters on local voxels of an image sequence where the filters are learned via an independent component analysis, maximising independence over spatial and temporal domains concurrently. The BSDF representation is formed by binarising filter responses which are then converted into codewords and summarised using histograms. A robust representation of the BSDF descriptor is finally obtained via a sparse representation approach yielding very discriminative features for classification. The effects of different hyper-parameters on performance including the number of filters, the number of scales, temporal depth, number of samples drawn are also investigated. The proposed approach is evaluated on the most commonly used dynamic texture databases and shown to perform very well compared to the existing methods.

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“…K-means [21], Codebook [22], and background reconstruction [23] are examples of clustering models. Furthermore, machine learning models are the state-of-the-art models that encompass various techniques like support vector machines (SVM) [24], robust subspace tracking [25], reconstructive and discriminative subspace learning techniques [26][27], deep learning neural networks [28][29] and convolutional neural networks (CCN) [30], which have been broadly embraced due to the massive development of hardware processing power [31]. Fusion of several models and strategies is another approach that has been adopted in many articles for better performance.…”

Section: Introductionmentioning

confidence: 99%

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Yasir

Ali

2023

Iraqi Journal of Science

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Background subtraction is the dominant approach in the domain of moving object detection. Lots of research has been done to design or improve background subtraction models. However, there are a few well-known and state-of-the-art models that can be applied as a benchmark. Generally, these models are applied to different dataset benchmarks. Most of the time, choosing an appropriate dataset is challenging due to the lack of dataset availability and the tedious process of creating ground-truth frames for the sake of quantitative evaluation. Therefore, in this article, we collected local video scenes of a street and river taken by a stationary camera, focusing on dynamic background challenges. We presented a new technique for creating ground-truth frames using modeling, composing, tracking, and rendering each frame. Eventually, we applied three promising algorithms used in this domain: GMM, KNN, and ViBe, to our local dataset. Results obtained by quantitative evaluations revealed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using a number of statistical metrics.

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Single-class SVM for dynamic scene modeling

Cited by 9 publications

References 16 publications

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Singer identification using perceptual features and cepstral coefficients of an audio signal from Indian video songs

Sparse binarised statistical dynamic features for spatio-temporal texture analysis

New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

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