Perceptual Image Representations for Support Vector Machine Image Coding

2011 ◽  
pp. 303-324
Author(s):  
Juan Gutiérrez ◽  
Gabriel Gómez-Perez ◽  
Jesús Malo ◽  
Gustavo Camps-Valls
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
General Procedure ◽  
Image Representation ◽  
Rate Distortion ◽  
Human Perception ◽  
Human Vision ◽  
Support Vector ◽  
Perception Model ◽  
Image Representations

Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity zone of the support vector regression (SVR) at some specific image representation determines (a) the amount of selected support vectors (the compression ratio), and (b) the nature of the introduced error (the compression distortion). However, the selection of an appropriate image representation is a key issue for a meaningful design of the e-insensitivity profile. For example, in image coding applications, taking human perception into account is of paramount relevance to obtain a good rate-distortion performance. However, depending on the accuracy of the considered perception model, certain image representations are not suitable for SVR training. In this chapter, we analyze the general procedure to take human vision models into account in SVR-based image coding. Specifically, we derive the condition for image representation selection and the associated e-insensitivity profiles.

scholarly journals Basketball shooting technology based on acceleration sensor fusion motion capture technology

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Binbin Zhao ◽  
Shihong Liu
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Motion Capture ◽  
Visual Recognition ◽  
Gaussian Mixture ◽  
Human Vision ◽  
Support Vector ◽  
Background Removal ◽  
Graphics Processing ◽  
Basketball Shooting

AbstractComputer vision recognition refers to the use of cameras and computers to replace the human eyes with computer vision, such as target recognition, tracking, measurement, and in-depth graphics processing, to process images to make them more suitable for human vision. Aiming at the problem of combining basketball shooting technology with visual recognition motion capture technology, this article mainly introduces the research of basketball shooting technology based on computer vision recognition fusion motion capture technology. This paper proposes that this technology first performs preprocessing operations such as background removal and filtering denoising on the acquired shooting video images to obtain the action characteristics of the characters in the video sequence and then uses the support vector machine (SVM) and the Gaussian mixture model to obtain the characteristics of the objects. Part of the data samples are extracted from the sample set for the learning and training of the model. After the training is completed, the other parts are classified and recognized. The simulation test results of the action database and the real shot video show that the support vector machine (SVM) can more quickly and effectively identify the actions that appear in the shot video, and the average recognition accuracy rate reaches 95.9%, which verifies the application and feasibility of this technology in the recognition of shooting actions is conducive to follow up and improve shooting techniques.

Natural Image Classification Based on Improved Support Vector Machine

2011 ◽  
Vol 58-60 ◽  
pp. 2387-2391
Author(s):  
Ying Jian Qi ◽  
Zhi Wei Ou ◽  
Bin Zhang ◽  
Ting Zhan Liu ◽  
Ying Li
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Ant Colony Algorithm ◽  
Image Representation ◽  
Image Features ◽  
Ant Colony ◽  
Function Parameter ◽  
Support Vector ◽  
Natural Image ◽  
Improved Support Vector Machine

Local image representation based natural image classification is an important task. SIFT descriptors and bag-of-visterm (BOV)method have achieved very good results. Many studies focused on improving the representation of the image, and then use the support vector machine to classify and identify the image category. However, due to support vector machine its own characteristics, it shows inflexible and slower convergence rate for large samples，with the selection of parameters influencing the results for the algorithm very much. Therefore, this paper will use the improved support vector machine algorithm be based on ant colony algorithm in classification step. The method adopt dense SIFT descriptors to describe image features and then use two levels BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks.

scholarly journals TESTING THE PERFORMANCES OF DIFFERENT IMAGE REPRESENTATIONS FOR MASS CLASSIFICATION IN DIGITAL MAMMOGRAMS

2006 ◽  
Vol 17 (01) ◽  
pp. 113-131 ◽  
Author(s):  
E. ANGELINI ◽  
R. CAMPANINI ◽  
E. IAMPIERI ◽  
N. LANCONELLI ◽  
M. MASOTTI ◽  
...  
Keyword(s):  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Image Representation ◽  
Normal Breast ◽  
South Florida ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Positive Fraction ◽  
Image Representations ◽  
Wavelet Image

The classification of tumoral masses and normal breast tissue is targeted. A mass detection algorithm which does not refer explicitly to shape, border, size, contrast or texture of mammographic suspicious regions is evaluated. In the present approach, classification features are embodied by the image representation used to encode suspicious regions. Classification is performed by means of a support vector machine (SVM) classifier. To investigate whether improvements can be achieved with respect to a previously proposed overcomplete wavelet image representation, a pixel and a discrete wavelet image representations are developed and tested. Evaluation is performed by extracting 6000 suspicious regions from the digital database for screening mammography (DDSM) collected by the University of South Florida (USF). More specifically, 1000 regions representing biopsy-proven tumoral masses (either benign or malignant) and 5000 regions representing normal breast tissue are extracted. Results demonstrate very high performance levels. The area Az under the receiver operating characteristic (ROC) curve reaches values of 0.973 ± 0.002, 0.948 ± 0.004 and 0.956 ± 0.003 for the pixel, discrete wavelet and overcomplete wavelet image representations, respectively. In particular, the improvement in the Az value with the pixel image representation is statistically significant compared to that obtained with the discrete wavelet and overcomplete wavelet image representations (two-tailed p-value < 0.0001). Additionally, 90% true positive fraction (TPF) values are achieved with false positive fraction (FPF) values of 6%, 11% and 7%, respectively.

Automatic seam pucker evaluation using support vector machine classifiers

2019 ◽  
Vol 31 (1) ◽  
pp. 2-15
Author(s):  
Ning Zhang ◽  
Ruru Pan ◽  
Lei Wang ◽  
Shanshan Wang ◽  
Jun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Human Vision ◽  
Support Vector ◽  
Content Type ◽  
Finite Samples ◽  
Linear Relationships ◽  
Novel Method ◽  
Occurrence Matrix ◽  
Seam Pucker

Purpose The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation. Design/methodology/approach Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation. Findings The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation. Originality/value There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

scholarly journals Image Retrieval using Autocorrelation Based Chordiogram Image Descriptor and Support Vector Machine

2019 ◽  
Vol 8 (3) ◽  
pp. 6019-6023 ◽  
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Image Representation ◽  
Haar Wavelet ◽  
Research Area ◽  
Support Vector ◽  
Image Descriptor ◽  
Evolving Systems ◽  
Significant Research ◽  
Benchmark Datasets

Nowadays, evolving systems for indexing and organizing images is more important due to proliferation of images in all domains and it has made content-based image retrieval (CBIR) as significant research area. This paper uses autocorrelation based chordiogram image descriptor (ACID) for effective image representation and Support vector machine (SVM) for effective image classification. The ACID of images is computed from Haar wavelet based multiresolution domain and it exploits shape, texture and geometric details. The proposed combination of ACID and SVM is highly compatible and is comprehensively tested on benchmark datasets namely Gardens Point Walking and St. Lucia and experimental results prove that proposed combination outperforms significantly in terms of precision and recall

scholarly journals Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM

2020 ◽  
Vol 7 (2) ◽  
pp. 379
Author(s):  
Agung Wahyu Setiawan ◽  
Alfie R. Ananda
Keyword(s):  
Support Vector Machine ◽  
Test Data ◽  
Palm Oil ◽  
Image Contrast ◽  
Human Vision ◽  
Training Data ◽  
Training Dataset ◽  
Support Vector ◽  
Light Sources ◽  
Maturity Level

<p class="Abstrak">Salah satu permasalahan utama dalam industri kelapa sawit adalah proses sortasi Tandan Buah Segar (TBS) di pabrik kelapa sawit. Parameter yang digunakan dalam sortasi TBS adalah jumlah brondolan kelapa sawit. Pada saat ini, sortasi dilakukan oleh <em>grader</em> yang bersifat subyektif dan sering kali tidak konsisten. Hal ini terjadi karena keterbatasan penglihatan dan kemampuan manusia untuk mengolah informasi jumlah brondolan setiap TBS dalam waktu yang terbatas. Oleh karena itu, pada penelitian ini dikembangkan sistem penilaian kematangan TBS kelapa sawit berbasis spektroskopi dan nilai kontras citras. Sumber cahaya yang digunakan pada penelitian ini adalah lampu berjenis <em>Light-emitting Diode</em> (LED) dengan panjang gelombang 680 dan 750 nm. Akuisisi citra TBS dilakukan dengan menggunakan kamera DSLR yang telah dimodifikasi. sehingga diperoleh dua citra TBS pada panjang gelombang 680 dan 750 nm. Kemudian, dilakukan perhitungan nilai kontras kedua citra tersebut. Dalam penelitian ini, terdapat 24 TBS yang digunakan sebagai data latih, dengan komposisi 10 TBS matang dan 14 TBS mentah. Data uji yang digunakan berjumlah 77 TBS yang terdiri dari 38 matang dan 39 mentah. Pada penelitian ini, <em>Support Vector Machine</em> (SVM) digunakan sebagai metode klasifikasi. Akurasi data latih yang diperoleh adalah 66,67%. Sedangkan akurasi data uji dari sistem yang dikembangkan dalam penelitian ini adalah 57,14%. Hasil yang diperoleh ini masih perlu diperbaiki untuk meningkatkan akurasi sistem dengan cara menambah jumlah data, baik data latih maupun uji, serta menggunakan pembelajaran mesin.</p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"><em>One of the main problems in the palm oil industry is the grading of Fresh Fruit Bunches (FFB) in the palm oil mills. The parameter used for the process is the number of fruitlets detached from the bunch. Nowadays, the FFB grading is conducted by graders which is subjective and often inconsistent due to the limitation of human vision and ability to process information on the number of fruitlets detached per FFB in a very limited time. Therefore, this study developed a grading system to assess and estimate the FFB maturity based on spectroscopy and image contrast value. From the literature review, visible light and NIR spectrum in 680 and 780 nm can be used as light sources to detect the maturity level of FFB. DSLR camera is used to acquire the FFB image. Using this scheme, two FFB images in 680 and 750 nm are obtained. The next process is to calculate the image contrast. In this research, there are 24 FFB that are used as training data that consists of 10 ripe and 14 unripe. A total of 77 FFB are used as test data that consists of 38 ripe and 39 unripe. Support Vector Machine (SVM) is used in this research to classify the maturity level of FFB. The accuracy of the training dataset is 66.67%. Meanwhile, the accuracy of the test data is 57.14%. Future works will focus on enhancing accuracy of the system through increasing the number of training and testing data using machine learning.</em></p>

Perceptual Adaptive Insensitivity for Support Vector Machine Image Coding

2005 ◽  
Vol 16 (6) ◽  
pp. 1574-1581 ◽  
Author(s):  
G. Gomez-Perez ◽  
G. Camps-Valls ◽  
J. Gutierrez ◽  
J. Malo
Keyword(s):  
Support Vector Machine ◽  
Image Coding ◽  
Support Vector ◽  
Machine Image
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