A novel image retrieval method based on multi-trend structure descriptor

Zhao, Meng; Zhang, Huaxiang; Sun, Jiande

doi:10.1016/j.jvcir.2016.02.016

Cited by 52 publications

(42 citation statements)

References 29 publications

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“…Since there are two feature vectors in the proposed methods, the similarity weighting constants in Equation (17) will be successively set to {λ 1 = 1, λ 2 = 0}, {λ 1 = 0, λ 2 = 1}, and {λ 1 = 1, λ 2 = 1}, corresponding to the situations that only color feature, only texture feature, and their combination are respectively used for retrieval. In addition, several other image retrieval algorithms proposed in recent years are used in comparison, including image structure elements' histogram [23] (SEH), multi-trend structure descriptor [28] (MTSD), local structure descriptor [29] (LSD), and integrated LBP-based approach [30]. Tables 1-3 show the experimental results of the proposed methods on Corel-1k with {λ 1 = 1, λ 2 = 0}, {λ 1 = 0, λ 2 = 1}, and {λ 1 = 1, λ 2 = 1}, respectively.…”

Section: Resultsmentioning

confidence: 99%

Effective Image Retrieval Using Texture Elements and Color Fuzzy Correlogram

Yang

Hao

2017

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Abstract:Image low-level information, such as color, texture, and shape, were generally dealt with separately and combined together gruffly. Their image-describing effect in image retrieval was weakened. This paper determines and extracts one group of texture elements from images to mainly express the image texture information and, during this procedure, the quantized HSV color information is added to develop the feature, Color Layer-Based Texture Elements Histogram (CLBTEH). Furthermore, Color Fuzzy Correlogram (CFC) is put forward and employed for further extraction of color features. The performance of the proposed approach is evaluated on different image databases, including Corel-1k, Corel-10k, and USPTex1.0, and it was found that the experimental results of the proposed approach are encouraging in comparison with similar algorithms.

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Section: Resultsmentioning

confidence: 99%

Effective Image Retrieval Using Texture Elements and Color Fuzzy Correlogram

Yang

Hao

2017

Information

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“…Since the night working environment is very complex, and many factors can influence the quality of night vision images, such as the distance between target and light source, exposure time, the power of the artificial light source, and so on. To better process night vision images, further work should focus on getting more accurate image information, providing the precision signal for target recognition [26] , video or image processing [27,28] , and servo control [29] , etc.…”

Section: Discussionmentioning

confidence: 99%

Preprocessing method of night vision image application in apple harvesting robot

Jia¹,

Zheng²,

Zhao³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Due to the low working efficiency of apple harvesting robots, there is still a long way to go for commercialization. The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots, this study focused on the extended operating time and proposed a round-the-clock operation mode. Due to the influences of light, temperature, humidity, etc., the working environment at night is relatively complex, and thus restricts the operating efficiency of the apple harvesting robot. Three different artificial light sources (incandescent lamp, fluorescent lamp, and LED lights) were selected for auxiliary light according to certain rules so that the apple night vision images could be captured. In addition, by color analysis, night and natural light images were compared to find out the color characteristics of the night vision images, and intuitive visual and difference image methods were used to analyze the noise characteristics. The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night, and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise. The preprocessing method can provide a theoretical and technical reference for subsequent image processing.

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“…The decomposition of HSV image is done up to the level of 3, which is found to be more optimum in [41]. The image at level 3 significantly reduces the computation cost as well preserves the accuracy of MTSD as in [38].…”

Section: Proposed Feature Extractionmentioning

confidence: 99%

“…Uniform quantization ui s performed for each sub-image as illustrated in [38]. The quantization process produces 12, 3 and 3 levels for color, 20 and 9 levels for texture and shape respectively [38]. Both texture and shape information are exploited from V channel.…”

Section: Proposed Feature Extractionmentioning

confidence: 99%

“…Wide-ranging review for CBMIR is presented on [1, 31 -37]. Recently, MTSD, which exploits rich texture, shape and color information in the form of trends from local level structure, have shown considerable performance in image retrieval task [38]. Later, inspired by the efficacy of MTSD, we combined the efficiency of MTSD with fuzzy k nearest neighborhood [39] and fuzzy SVM [40] respectively for CBMIR.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Trend Structure Descriptor At Micro-Level For Histological Image Retrieval

Natarajan*¹

2019

IJRTE

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Abstract: Since hospitals are generating and using image data extensively, medical im age databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided int o number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results. Pattern analysis, Image retrieval and classification. His research interests include Machine learning algorithms and Medical imageanalysis. He has published more than 25 papers in various International journals and more than 15 papers in national and international conferences. He has been on the reviewing and editorial board of many reputed journals.

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A novel image retrieval method based on multi-trend structure descriptor

Cited by 52 publications

References 29 publications

Effective Image Retrieval Using Texture Elements and Color Fuzzy Correlogram

Effective Image Retrieval Using Texture Elements and Color Fuzzy Correlogram

Preprocessing method of night vision image application in apple harvesting robot

Multi-Trend Structure Descriptor At Micro-Level For Histological Image Retrieval

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