Improved CBIR system using Edge Histogram Descriptor (EHD) and Support Vector Machine (SVM)

Vikhar, Pradnya; Karde, Pravin

doi:10.1109/ictbig.2016.7892678

Cited by 17 publications

(5 citation statements)

References 4 publications

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“…According to the experimental results, Hu's seven moments seem to do best in almost all classes of the CT database in terms of accuracy. In [32], EH features were used in combination with color autocorrelogram, color moment, and Gabor wavelet transforms to train the SVM for classification.…”

Section: Related Workmentioning

confidence: 99%

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Khan

Javed²,

Mahmood

et al. 2021

IEEE Access

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Social media platforms such as Twitter, Facebook, and Flicker, and the evolution of digital image capturing devices have resulted in the generation of a massive number of images. Thus, we experienced exponential growth in digital image repositories in the last decade. Content-based image retrieval (CBIR) has been extensively employed to reduce the dependency on textual annotations for image searching. Effective feature descriptor is mandatory to retrieve the most relevant images from the repository. Additionally, CBIR methods often experience the semantic gap problem, which must be addressed. In this paper, we propose a novel texture feature descriptor, Directional Magnitude Local Hexadecimal Patterns (DMLHP), based on texture orientation and magnitude to retrieve the most relevant images. The objective of the proposed feature descriptor is to examine the relationship of neighboring pixels with their adjacent neighbors based on texture orientation and magnitude. Our DMLHP texture descriptor is capable of capturing the texture and semantic information of the images effectively with the same visual content. Furthermore, the proposed method employs a learning-based approach to lessen the semantic gap problem and to improve the understanding of the contents of query images to retrieve the relevant images. The presented descriptor provides remarkable results by achieving the average retrieval precision (ARP) of 66%, 92%, 83%, average retrieval recall (ARR) of 66%, 92%, 83%, average retrieval specificity (ARS) of 99%, 99%, 76%, and average retrieval accuracy (ARA) of 98%, 99%, 85% on the AT&T, MIT Vistex, and Brodatz Texture image repositories, respectively. Our experiments reveal that the proposed DMLHP descriptor achieves far better performance, i.e., 95% on AT&T, 92% on BT, and 99% on MIT Vistex, when used with a learning-based approach over a non-learning-based approach (similarity measure). Experimental results show that the proposed texture descriptor outperforms state-of-the-art descriptors such as LNIP, LTriDP, LNDP, LDGP, LEPSEG, and CSLBP for CBIR.INDEX TERMS Content-based image retrieval, Orientation-based pattern, Magnitude-based pattern, Similarity-based approach, Learning-based approach.

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Section: Related Workmentioning

confidence: 99%

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Khan

Javed²,

Mahmood

et al. 2021

IEEE Access

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“…SVM has also been used to obtain a precise classification accuracy. Segmentation based Fractal Texture Analysis (SFTA) can also be employed as a texture analysis technique [23]. Again, for classification purpose SVM classifier has been used.…”

Section: B Related State-of-the-art Workmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.

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“…Images can significantly differ under diverse lighting and angles, leading to diminished retrieval accuracy, as indicated by Shen et al [8]. While scholars have attempted to utilize methods like the edge histogram descriptor (EHD) [9] and color layout descriptor (CLD) [10] to extract feature vectors from images, these approaches fall short of providing a comprehensive image characterization, thus resulting in low retrieval accuracy. Efforts to establish secure image retrieval schemes have incorporated the scale-invariant feature transform (SIFT) for extracting local image features [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Encrypted Image Retrieval in Cloud Environment

Chen,

Huang

2023

Mathematics

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With the growing emphasis on privacy awareness, there is an increasing demand for privacy-preserving encrypted image retrieval and secure image storage on cloud servers. Nonetheless, existing solutions exhibit certain shortcomings regarding retrieval accuracy, the capacity to search large images from smaller ones, and the implementation of fine-grained access control. Consequently, to rectify these issues, the YOLOv5 technique is employed for object detection within the image, capturing them as localized images. A trained convolutional neural network (CNN) model extracts the feature vectors from the localized images. To safeguard the encrypted image rules from easy accessibility by third parties, the image is encrypted using ElGamal. In contrast, the feature vectors are encrypted using the skNN method to achieve ciphertext retrieval and then upload this to the cloud. In pursuit of fine-grained access control, a role-based multinomial access control technique is implemented to bestow access rights to local graphs, thereby achieving more nuanced permission management and heightened security. The proposed scheme introduces a comprehensive cryptographic image retrieval and secure access solution, encompassing fine-grained access control techniques to bolster security. Ultimately, the experiments are conducted to validate the proposed solution’s feasibility, security, and accuracy. The solution’s performance across various facets is evaluated through these experiments.

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Improved CBIR system using Edge Histogram Descriptor (EHD) and Support Vector Machine (SVM)

Cited by 17 publications

References 4 publications

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Fine-Grained Encrypted Image Retrieval in Cloud Environment

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