Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine

Taheri, Fatemeh; Rahbar, Kambiz; Salimi, Pedram

doi:10.1007/s11042-022-13670-w

Cited by 14 publications

(7 citation statements)

References 49 publications

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“…It examined feature selection, extraction, and representation, incorporating deep learning techniques and researched 215 articles and highlights future research directions for CBIR's evolution. Author (24) introduced a content-based image retrieval model that combines low-level and mid-level features. The model's performance on various datasets was evaluated, with the best-evaluated results showing 99.4% precision.…”

Section: Contribution Of the Studymentioning

confidence: 99%

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Anish,

Thiyagarajan

2024

IJST

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Objective: An essential aspect of computer vision is content-based image retrieval (CBIR), which enables users to search for images based on their visual content instead of created annotations. Advances in technology have resulted in a significant rise in the complexity of multimedia content and the emergence of new research fields centered on similar multimedia material retrieval. The efficacy of retrieval is impacted by the limits of the present CBIR systems, which result from overlooked algorithms and computing restrictions. Methods: This research introduces a novel approach employing the Siamese Edge Attention Layered Convonet (SEAL Convonet) for Image Retrieval. We utilize the CBIR image dataset through Gaussian smoothing to enhance image quality for data preprocessing and the Canny Edge Detector (CED) for edge detection, following pre-processing. The Histogram of Oriented Gradients (HOG) is used for feature extraction to extract complex textures and patterns from the images. Findings: This approach is implemented and tested through simulations as well as the results indicate a substantial positive deviation in the performance and retrieval of the images compared to existing methods. The performance metrics are accuracy (97 %), precision (94 %), recall (91 %), F1-Score (97 %), False Positive Rate (FNR) (0.0013), Matthew's correlation coefficient (MCC) (0.85), and False Negative Rate (FPR) (0.0036) show the measurements of this proposed model. Application: The state of the art in this work is researching the influence of optimizers on the accuracy process, as indicated by the findings. Keywords: CBIR, Gaussian smoothing, Canny Edge Detector, Histogram of oriented gradients (HOG), Siamese Edge Attention Layered convonet (SEAL Convonet), Database

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Section: Contribution Of the Studymentioning

confidence: 99%

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Anish,

Thiyagarajan

2024

IJST

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“…The selected channels are stacked to form a tensor of the C × 56 × 56 shape, where C is the number of selected channels. Another method proposed for use in CBIR is feature vector extraction, which is a combination of low-level and midlevel image features (LB-CBIR) [40]. The extraction of low-level image features (color, shape, and texture) was performed using auto-correlogram, Gabor wavelet transform, and multi-level fractal dimension analysis.…”

Section: Related Workmentioning

confidence: 99%

Highly compressed image representation for classification and content retrieval

Łażewski,

Cyganek

2024

ICA

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In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.

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“…The proposed model achieved 96% overall precision. The authors of [55] used three datasets, "Corel 1k", "Corel 5K", and Caltech 256, for the extraction of a similar image in the domain of content-based image retrieval. The proposed methodology provides 99.4% precision on the Corel1K dataset.…”

Section: Comparison With Existing Techniquesmentioning

confidence: 99%

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

2023

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The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.

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Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine

Cited by 14 publications

References 49 publications

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

Highly compressed image representation for classification and content retrieval

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

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