Color‐Texture‐Based Image Retrieval System Using Gaussian Markov Random Field Model

Tsai, Meng‐Hsiun; Chan, Yung-Kuan; Wang, Jiun-Shiang; Guo, Shu-Wei; Wu, Jiunn-Lin

doi:10.1155/2009/410243

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…This strategy has also been accepted as one of the important retrieval mechanisms in some famous image retrieval systems, such as query by image content (QBIC). 12,13 Some other similar works [14][15][16][17] could be found in this research. Although these works simultaneously take discriminative information and texture features into consideration, problems such as computational complexity and definition of weight parameters with combinational features are still an open question.…”

Section: Introductionsupporting

confidence: 54%

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Improved color texture descriptors for remote sensing image retrieval

Shao

Zhou

Zhang

et al. 2014

J. Appl. Remote Sens

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Abstract. Texture features are widely used in image retrieval literature. However, conventional texture features are extracted from grayscale images without taking color information into consideration. We present two improved texture descriptors, named color Gabor wavelet texture (CGWT) and color Gabor opponent texture (CGOT), respectively, for the purpose of remote sensing image retrieval. The former consists of unichrome features computed from color channels independently and opponent features computed across different color channels at different scales, while the latter consists of Gabor texture features and opponent features mentioned above. The two representations incorporate discriminative information among color bands, thus describing well the remote sensing images that have multiple objects. Experimental results demonstrate that CGWT yields better performance compared to other state-of-the-art texture features, and CGOT not only improves the retrieval results of some image classes that have unsatisfactory performance using CGWT representation, but also increases the average precision of all queried images further. In addition, a similarity measure function for proposed representation CGOT has been defined to give a convincing evaluation. © 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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Section: Introductionsupporting

confidence: 54%

“…(17) has similar form but different meanings as similarity measure Eq. (16). Since Gabor texture and opponent feature constitute CGOT representation, distance measure Eq.…”

Section: Similarity Measurementioning

confidence: 99%

Improved color texture descriptors for remote sensing image retrieval

Shao

Zhou

Zhang

et al. 2014

J. Appl. Remote Sens

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show abstract

“…Texture proper define surface smoothness or roughness and patterns associated with it. Popular methods for texture analysis are: Gabor filter which analysis the frequencies and orientation in local regions to extract texture pattern in images and is argued to be similar to human vision system in its function (10) , Markov random field uses probabilistic models to study the spatial correlation among neighbouring pixels (11) , edge histogram method focuses on the edges evaluating edge distribution, orientation, width etc. (12) .…”

Section: Global Featuresmentioning

confidence: 99%

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Khan,

Bhat

2023

IJST

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Objective:The objective of this study is to conduct a comprehensive review of existing research and literature in the field of Content-Based Image Retrieval (CBIR). This review highlights the key challenges associated with the extraction and representation of visual semantics of images. This paper discusses the measure used computing similarity and ranking of retrieved images by CBIR system. The review discusses limitation of traditional approaches and also highlights the challenges with the current deep learning methods in semantic feature representation, defining the similarity metrics and indexing. This paper also highlights scalability and generalization challenges in implementing real environment. Methods: A thorough literature review was conducted on wellestablished databases, including Scopus, Web of Science, IEEE Xplore, ACM, and Science Direct, employing appropriate keywords. Mention the period of coverage. Pertinent search terms encompassed local feature representation, global feature representation, low-level features, high level features, semantic gap, image embeddings, handcrafted features, deep learning, image descriptors, similarity, and image indexing, with the aim of exploring content-based image retrieval systems. Comparative analysis was performed on the chosen articles, taking into account factors such as algorithms, methodologies, datasets, and evaluation metrics. The results discussed using comparative analysis, ensuring a comprehensive overview of recent literature on content-based image retrieval, offering valuable insights and highlighting emerging trends in the field. Findings : The research uncovers the novelty in the realm of contentbased image retrieval (CBIR) by highlighting the challenge of high-level visual semantics when comparing images, as perceived by humans. It emphasizes that feature extraction methods and choices significantly influence CBIR system performance, stressing the importance of selecting suitable features and similarity measures based on image dataset characteristics and application requirements. The study underscores the persistent obstacle of the semantic gap between low-level visual features and high-level semantic concepts, encouraging exploration of diverse approaches like deep learning, relevance feedback, and ontology-based methods to bridge this gap. Particularly, deep learnhttps://www.indjst.org/

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“…(2) Information extraction: firstly, the features of each resource file captured by the crawler are extracted as a vector set. Then these features are converted into semantic information through the technique of structural analysis, noise reduction, duplicate content elimination, and text extraction [29][30][31]. Lastly, the semantic information is broken down into the subject tag, the concept tag, the instance tag, and label texts.…”

Section: Information Collection Module Information Collectionmentioning

confidence: 99%

Domain-Oriented Subject Aware Model for Multimedia Data Retrieval

Wang

2013

Mathematical Problems in Engineering

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With the increment of the scale of internet information as well as the cross-correlation interaction, how to achieve accurate retrieval of multimedia data is an urgent question in terms of efficiently utilizing information resources. However, existing information retrieval approaches provide only limited capabilities to search multimedia data. In order to improve the ability of information retrieval, we propose a domain-oriented subject aware model by introducing three innovative improvements. Firstly, we propose the text-image feature mapping method based on the transfer learning to extract image semantics. Then we put forward the annotation document method to accomplish simultaneous retrieval of multimedia data. Lastly, we present subject aware graph to quantify the semantics of query requirements, which can customize query threshold to retrieve multimedia data. Conducted experiments show that our model obtained encouraging performance results.

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Color‐Texture‐Based Image Retrieval System Using Gaussian Markov Random Field Model

Cited by 7 publications

References 21 publications

Improved color texture descriptors for remote sensing image retrieval

Improved color texture descriptors for remote sensing image retrieval

Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review

Domain-Oriented Subject Aware Model for Multimedia Data Retrieval

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