A Review on Image Retrieval Techniques

S, Meharban M; Priya, Dr.S.Manju

doi:10.9756/bijaip.8136

Cited by 14 publications

(12 citation statements)

References 12 publications

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“…The most prominent features of the image only will be extracted after feature extraction analysis has been done by the system [28]. One of the key attributes of an image region that has been segmented is the shape of the region and it is a crucial role in representing the effectiveness of a robust image retrieval system [29]. The aim of feature extraction is to extract some sets of features in order to minimize the image retrieval rate.…”

Section: Feature Extractionmentioning

confidence: 99%

Region of interest-based image retrieval techniques: a review

Jan

Zainal

Jamaludin

2020

IJ-AI

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<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

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Section: Feature Extractionmentioning

confidence: 99%

Region of interest-based image retrieval techniques: a review

Jan

Zainal

Jamaludin

2020

IJ-AI

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“…Thus, the scalability problem is appearing because of geometrical features infinity in range. The problem is alleviated via normalizing the feature vector as follows; 5 where maxCir and minCir are the maximum and minimum circularity of the object region respectively. It is being noted that, the notation Cirnom is the same of rectangularity.…”

Section: Rectangularitymentioning

confidence: 99%

“…In contrast, the high-level techniques are active to retrieve patterns by scanning whole image. The semantic digital image retrieval is one the talented exploration field where several researchers consider approaches either for image analysis [5,6,7,8] or image retrieval [9,10,11,12]. Most techniques of image retrieval usually used text metadata that relied on the description oh image's textual [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Shape and Geometric Features-based Semantic Image Retrieval Using Multi-class Support Vector Machine

Malki

2017

Preprint

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“…With the development of multimedia and the pervasiveness of mobile devices, a diverse array of images has been accreted in various professional fields, such as medicine, military affairs, criminal investigation, ecommerce, and smart logistics [1]. The organization, management, and retrieval of these massive images with high efficiency have become popular topics for academics worldwide.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

2022

Teh. vjesn.

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A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small-and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 -Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small-and medium-scale CBIR systems with inadequate samples.

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A Review on Image Retrieval Techniques

Cited by 14 publications

References 12 publications

Region of interest-based image retrieval techniques: a review

Region of interest-based image retrieval techniques: a review

Shape and Geometric Features-based Semantic Image Retrieval Using Multi-class Support Vector Machine

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

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