Application of SVM and ANN for image retrieval

Wong, Wai-Tak; Hsu, Sheng-Hsun

doi:10.1016/j.ejor.2005.08.002

Cited by 81 publications

(34 citation statements)

References 21 publications

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“…The total number of classifiers is N (N − 1)/2 for an N-class problem [14]. As the recognition method using 1-v-1 for a multiclass problem, Max-Win algorithm is used.…”

Section: Mapped Vectorsmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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“…The total number of classifiers is N (N − 1)/2 for an N-class problem [14]. As the recognition method using 1-v-1 for a multiclass problem, Max-Win algorithm is used.…”

Section: Mapped Vectorsmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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show abstract

“…Kuo et al [19] combined the AIS algorithm with a fuzzy neural network (FNN) to increase the accuracy of an RFID-based positioning system. This work develops an efficient SVM [20][21][22][23][24][25] method that is based on the AIS algorithm (AISSVM) for diagnosing ultrasound images of breast tumors. The proposed CAD system simultaneously performs parameter tuning and feature selection, and thus achieves high classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Wu¹,

Lin²,

Moon

2015

J Digit Imaging

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A rapid and highly accurate diagnostic tool for distinguishing benign tumors from malignant ones is required owing to the high incidence of breast cancer. Although various computer-aided diagnosis (CAD) systems have been developed to interpret ultrasound images of breast tumors, feature selection and the setting of parameters are still essential to classification accuracy and the minimization of computational complexity. This work develops a highly accurate CAD system that is based on a support vector machine (SVM) and the artificial immune system (AIS) algorithm for evaluating breast tumors. Experiments demonstrate that the accuracy of the proposed CAD system for classifying breast tumors is 96.67 %. The sensitivity, specificity, PPV, and NPV of the proposed CAD system are 96. 67, 96.67, 95.60, and 97.48 %, respectively. The receiver operator characteristic (ROC) area index A z is 0.9827. Hence, the proposed CAD system can reduce the number of biopsies and yield useful results that assist physicians in diagnosing breast tumors.

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“…The basic idea of SVM is to map data into a high-dimensional space and to find the hyperplane of the different classes with the maximum margin between them. SVMs have been used for image classification, [23][24][25][26] soil moisture estimation, 27 image retrieval, 28 and impervious surface estimation 29,30 for remote sensing studies in recent years. The theory of SVM has been extensively described in the literature.…”

Section: Support Vector Machinementioning

confidence: 99%

Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

Sun

et al. 2013

J. Appl. Remote Sens

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Abstract. The objective of this paper is to present a case study to derive land use/land cover (LULC) maps and investigate the long-term effects of LULC change on surface runoff in the fast urbanizing Beijing city. The LULC maps were derived from Landsat TM/ETM+ imagery (acquired in 1992, 1999, 2006, and 2009) using support vector machine method. A long-term hydrologic impact assessment model was applied to assess the impact of LULC change on surface runoff. Results indicated that the selected study area experienced rapid urbanization from 1992 to 2009. Because of urbanization, from 1992 to 2009, modeled runoff increased 30% for the whole area and 35% for the urban portion. Our results also indicated that the runoff increase was highly correlated with urban expansion. A strong relationship (R 2 ¼ 0.849) was observed between the impervious surface percent and the modeled runoff depth in the study area. In addition, a strong positive relationship was observed between runoff increase and percentage of urban areas (R 2 ¼ 0.997 for the whole area and R 2 ¼ 0.930 for the urban portion). This research can provide a simple method for policy makers to assess potential hydrological impacts of future urban planning and development activities. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Application of SVM and ANN for image retrieval

Cited by 81 publications

References 21 publications

Intelligent Fish Freshness Assessment

Intelligent Fish Freshness Assessment

An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images

Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

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