Multi-level colored directional motif histograms for content-based image retrieval

Pradhan, Jitesh; Ajad, Ashok; Pal, Arup Kumar; Banka, Haider

doi:10.1007/s00371-019-01773-9

Cited by 22 publications

(7 citation statements)

References 47 publications

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“…According to the results in Table 8, the best result is achieved by the proposed approach for both top-10 and top-20. All the tested state-of-the-art techniques, except technique in Al-Jubouri and Du [7], they tested their approach either for top-10 or for top-20, and this is why in Table 8 some cells do not contain the ARP and ARR results.…”

Section: Resultsmentioning

confidence: 99%

“…The reported results of the work revealed that this proposed technique achieved a precision rate of 73.5% for the top-20. Pradhan et al, in 2019, developed a new CBIR scheme based on multi-level colored directional motif histogram [7]. The proposed scheme extracts local structural features at three different levels.…”

Section: Huang Et Al Proposed a New Cbir Technique In 2010mentioning

confidence: 99%

“…(1) It leads to inaccurate results when human has been doing datasets annotation process wrongly, (2) single keyword of image information is not efficient to transfer the overall image description, (3) it is based on manual annotation of the images, which is time consuming [5], [7].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

Salih

Abdulla²

2021

UHD J SCI TECH

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Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Huang Et Al Proposed a New Cbir Technique In 2010mentioning

confidence: 99%

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An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

Salih

Abdulla²

2021

UHD J SCI TECH

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“…In [41], Manisha et al propose an IR technique named LNDP, the innovative feature descriptor transforms and expresses the interrelations of all adjacent pixels in binary form. Pradhan et al [42] propose a multilevel colored directional motif histogram (MLCDMH) for designing a CBIR scheme to extract local structural features of different levels. Singhal et al [43] design a new texture feature (DLTCoP), which achieves higher speed performance.…”

Section: A Natural Scene Image Retrievalmentioning

confidence: 99%

A Light-Weighted Hypergraph Neural Network for Multimodal Remote Sensing Image Retrieval

Deng

Zhao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Moreover, to increase retrieval performance, better features must be extracted. While it may be possible to extract more complicated low-level characteristics from images, increasing the calculation time would increase the size of the feature vector and slow down retrieval speed (Pradhan et al, 2020;Alshehri et al, 2020). Deep learning is the important concept that has been shown to eliminate the semantic gap.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

Sanjeevaiah¹,

Reddy²,

Karthik³

et al. 2022

International Journal of Software Innovation

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In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.

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Multi-level colored directional motif histograms for content-based image retrieval

Cited by 22 publications

References 47 publications

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

A Light-Weighted Hypergraph Neural Network for Multimodal Remote Sensing Image Retrieval

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

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