Image Retrieval Using the Intensity Variation Descriptor

Zhao, Wei; Liu, Guanghai

doi:10.1155/2020/6283987

Cited by 18 publications

(5 citation statements)

References 55 publications

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“…is is similar to the perception of the human visual system and has been found to be particularly appropriate for texture representation and discrimination [15][16][17][18]. Liu et al proposed the intensity variation descriptor [19] and the gradient-structures histogram [20] to represent image content and used them for image retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…In digital image processing, color feature extraction, and edge detection play an important role in feature extraction and includes high-level concepts [40,41]. In applications of CBIR and object recognition, color can provide powerful information for feature extraction and representation [2,9]; even in the absence of shape information, combining color features with other visual features, such as textures, edge cues, and spatial attributes, is a popular technique to improve the image retrieval performance [19,20]. Color and edge cues are visual search components for stimuli perception that can express meaningful characteristics of images or scenes.…”

Section: The Multi-integration Features Model and Representationmentioning

confidence: 99%

“…e HSV color space can mimic the human color perception well; therefore, many researchers have used it for color quantization [6,8,10,19,20]. e HSV color space is based on the cylindrical coordinate system and is defined in terms of the three components of hue (H), saturation (S), and value (V), where S is the distance perpendicular to the rod, V is the central rod, and H is the angle around the rod [19,20,40,41]. In the proposed MIFH method, the color feature is extracted using color quantization.…”

Section: Color Feature Extraction and The Initial Featurementioning

confidence: 99%

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Image Retrieval Based on a Multi-Integration Features Model

Chu

Liu

2020

Mathematical Problems in Engineering

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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

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Section: Related Workmentioning

confidence: 99%

Section: The Multi-integration Features Model and Representationmentioning

confidence: 99%

Section: Color Feature Extraction and The Initial Featurementioning

confidence: 99%

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Image Retrieval Based on a Multi-Integration Features Model

Chu

Liu

2020

Mathematical Problems in Engineering

Self Cite

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“…Figure edges are groups of pixels with a high degree of dissimilarity that indicate crucial aspects of the image and carry information [10], [11], [12]. Edges are directly coupled with shape variations in the pixel intensity distribution [13], [14], [15]. Studies on extraction features, description, and recognition targets rely heavily on edge detection in image processing [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Andini¹,

Wanto²,

Devita³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Edge detection is fundamental to Figure processing. Edges include much information in a figure, including the object's location, shape, size, and information about its texture. Since edge detection is a critical component of Figure processing for object detection, comprehend algorithms for edge detection. This is because the edges define an item's contours, serve as a demarcation between the object and its backdrop, and serve as a demarcation between overlapping objects. That is, if the edges of an image can be identified accurately, all things can be found. The proposal of this paper is the use of the Canny Zerocross hybrid method to perform better edge detection based on comparative studies and the incorporation of the Canny way, which is considered one of the best edge detection methods, with the Zerocross way (cross zero) which is a derivative of the laplacian. In this paper, the research data used is the retinal image dataset-data obtained from STARE (Structured Analysis of the Retina). The Veterans Administration Medical Center in San Diego and the Shiley Eye Center (ECS) at the University of California provided Figures and clinical data from the retinal images. The experimental results of the comparative study show that the Zerocross edge detection technique is better than the Canny edge detection technique. Meanwhile, edge detection and image identification would be better when combining the two methods (hybrid) based on merging studies.

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“…In the last two decades, exponential advances are visible in digital image processing technologies, network facilities, data repository technologies, smartphones, and cameras. is has resulted in videos and multimedia data being generated, uploaded on the Internet, and shared through social media websites, leading to an explosion in the amount and complexity of digital data being generated, stored, transmitted, analyzed, and accessed [1]. Access to a desired image from the repository involves searching for images portraying specific types of objects or scenes, identifying a particular mood, or simply searching the exact pattern or texture.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Dewan¹,

Thepade²

2020

Applied Computational Intelligence and Soft Computing

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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

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Image Retrieval Using the Intensity Variation Descriptor

Cited by 18 publications

References 55 publications

Image Retrieval Based on a Multi-Integration Features Model

Image Retrieval Based on a Multi-Integration Features Model

Hybrid Canny Zerocross Method for Edge Detection in Retina Identification Cases

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

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