An integrated approach to region based image retrieval using firefly algorithm and support vector machine

Kanimozhi, T.; Latha, K.

doi:10.1016/j.neucom.2014.07.078

Cited by 61 publications

(26 citation statements)

References 53 publications

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“…In reality, the SVR-based machine learning technology has penetrated into various kinds of research fields, such as used for image retrieval [38] and quality evaluation [39]. Given a training dataset D ¼ fðx 1 ; y 1 Þ; …; ðx r ; y r Þg, where x i and y i are respectively a feature vector of f 01 À f 13 in Table 3 and the target output of the SIQM score derived from the i-th training image.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

Learning a blind quality evaluation engine of screen content images

et al. 2016

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a b s t r a c tWe in this paper investigate how to blindly predict the visual quality of a screen content image (SCI). With the popularity of multi-client and remote-controlling systems, SCIs and the relevant applications have been a hot research topic. In general, SCIs contain texts or graphics in cartoons, ebooks or captures of computer screens. As for blind quality assessment (QA) of natural scene images (NSIs), it has been well established since NSIs possess certain statistical properties. SCIs however do not have reliable statistic models so far and thus the associated blind QA task is hard to be addressed. Aiming at solving this problem, we first extract 13 perceptual-inspired features with the free energy based brain theory and structural degradation model. In order to avoid the overfitting and guarantee the independence of training and testing samples, we then collect 100,000 images and use their objective quality scores computed via a high-accuracy full-reference QA method for SCIs as labels, before learning a new blind quality measure from aforementioned 13 features to the objective quality score. Experimental results performed on a large-scale screen image quality assessment database (SIQAD) demonstrate that the proposed blind quality metric has a good correlation with human perception of quality, even superior to state-of-the-art full-, reduced-and no-reference QA algorithms.

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Section: Image Quality Evaluationmentioning

confidence: 99%

Learning a blind quality evaluation engine of screen content images

et al. 2016

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“…The third and last one is automatic image annotation which can implement the function of image retrieval by transforming the content retrieval into text retrieval [7,8,9]. In CBIR, extraction of unique features contained in image is one of the essential steps [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…T. Kanimozhi and K. Latha integrated support vector machine based learning with an evolutionary stochastic algorithm, taking the firefly algorithm as a relevance feedback approach into a region based image retrieval system. It reduced the semantic gap through optimized iterative learning and also provided a better exploration of solution space [11]. M. R. B and J. L etc.…”

Section: Introductionmentioning

confidence: 99%

Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning

Xia

Zhang

et al. 2017

Neurocomputing

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A method for automatic image annotation based on multi-feature fusion and multi-label learning algorithm was proposed in this paper. In the process of feature fusion, rotation-invariant uniform local binary pattern histogram distribution and counting of connected regions in image were extracted and utilized fully. Besides traditional n-order color moments and texture information, rotation-invariant uniform LBP histogram distribution, connected regions number, weighted histogram's integral were appended to image features which aided to improve the average precision. Based on multi-label learning k-nearest neighbor algorithm and Corel5k image data set, comparisons among different dimensional features combinations were made to show that the proposed method outperformed that of traditional one with only basic color moments and texture distribution. The average precision was showed to be improved from 0.2898 to 0.3954 in automatic image annotation in our experimental results.

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“…A more recent approach in biological inspired metaheuristic optimization algorithms is firefly algorithm (FFA) developed by Yang [38]. The FFA has been adjudged to be more efficient and robust in finding both local and global optima compared to other biological inspired optimization algorithms [39][40][41][42][43]. The prediction accuracy of the SVM model highly relies on proper determination of model parameters [44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

Gocić

Shamshirband

Razak

et al. 2016

Advances in Meteorology

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The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010) in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet), the firefly algorithm (SVM-FFA), and using the radial basis function (SVM-RBF), were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI.

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An integrated approach to region based image retrieval using firefly algorithm and support vector machine

Cited by 61 publications

References 53 publications

Learning a blind quality evaluation engine of screen content images

Learning a blind quality evaluation engine of screen content images

Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning

Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

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