Pipelined technique for image retrieval using texture and color

Srivastava, Divya; Goel, Surbhi; Agarwal, Suneeta

doi:10.1109/icpces.2017.8117611

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fundamentally a straight channel catches a certain recurrence content in the picture in any nearby district in the explicit course. Gabor change [128]- [130] has been generally utilized in satisfied based picture recovery. Gabor is appropriate for fixed signals as it depends on spatial reliance utilizing Fourier investigation.…”

Section: ) Transform Methodsmentioning

confidence: 99%

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Srivastava,

Singh,

Rajitha

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Consistently the web and online media producing terabytes of images. Furthermore, the picture stockpiling and important recovery is a difficult task. Content Based Image Retrieval (CBIR) is the most common way of recovering pertinent photos regarding query images from the assortment of images. CBIR profoundly relies upon three elements: determination, extraction, and portrayal of elements/features. The proposed review work discusses about these factors in detail. It begins with examining the need for CBIR and its applications. Subsequent sections discuss selecting features such as Color, Texture, Shape, and Descriptors. Feature extraction and their representation are analysed straightaway. Further, the investigation of the most recent papers and their techniques for CBIR is presented. A CBIR has also been applied using Deep Learning Techniques in recent years. An overview of these techniques is presented in this work. The proposed article is an outline of 214 papers covering everything conceivable analysis performed in the field of CBIR.

show abstract

Section: ) Transform Methodsmentioning

confidence: 99%

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Srivastava,

Singh,

Rajitha

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The colour features are very stable, and they are insensitive to rotation, translation, scale changes, and even various deformations. They showed strong robustness and is the most commonly used features of content-based image retrieval [10]. Colour histogram is the most commonly used colour features in image retrieval systems, while colour correlation diagram depicts the proportion of a certain number of colour pixels in an image, and also reflect the spatial correlation between different colours [11].…”

Section: Colour Feature Extractionmentioning

confidence: 99%

“…i,j∈ Y ij (10) In Equation (9), (i c ,j c ) represents the point where the total mass of the image plane image is concentrated; in Equation (10), λ is the NMI eigenvalue, Y ij is the binary image, and is the region of Y ij = 1 in the binary image. It can be seen that the eigenvalue λ of the NMI is the ratio of the moment of inertia of the binary image quality around its centre of gravity to its mass.…”

Section: Nmi Featurementioning

confidence: 99%

“…According to the result of sorting the ED value of the image, the ranking of the image is determined. X represents the feature vector of the image to be retrieved, y represents the feature vector of any image in the image database, and i = 1, 2, 3, 4, respectively, represent the four features of the image [10]. The Euclidean distance formula is as follows:…”

Section: Similarity Measurementioning

confidence: 99%

See 1 more Smart Citation

Image retrieval based on colour and improved NMI texture features

Wang

Qin

2019

Automatika

View full text Add to dashboard Cite

This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion.

show abstract

“…In this work, a good CBIR system is designed by using the texture feature. In an image retrieval system all the three features color, texture, and shape play an important role [3]. Texture identification is an important part of the research in image retrieval and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Texture Based Image Retrieval Using Semivariogram and Various Distance Measures

Narayan¹,

Murthy²

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

In a content-based image retrieval system(CBIR) feature classification,identification, and extraction play an important role. The retrieval of images using a single feature is a challenging task in CBIR systems. The high retrieval rates are reported based on combining multiple features, multiple algorithms and preprocessing steps, feature classification, and segmentation because the image retrieval are mainly based on the content in an image. This paper presents a texture feature extraction for the image retrieval system from semivariogram and robust semivariogram technique. A semivariogram is a statistical approach that provides the textural information based on the lag distance 'h'.The proposed method is tested on various standard image databases such as Corel-1k, Corel-10k, and Coil-100 database. The semivariogram and robust semivariogram methods are tested for the Corel 1k database using four distance metrics i.e. Euclidean, Manhatten, Canberra, and Chord distance to check which distance measure is appropriate for the CBIR system.The proposed method is also tested on three types of databases to investigate the performance of the CBIR system. The Matlab simulation results show that the effective performance of the system with Euclidean distance.

show abstract

Pipelined technique for image retrieval using texture and color

Cited by 4 publications

References 15 publications

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Image retrieval based on colour and improved NMI texture features

Texture Based Image Retrieval Using Semivariogram and Various Distance Measures

Contact Info

Product

Resources

About