Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval

Al-Azawi, Mohammad

doi:10.5565/rev/elcvia.1325

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…Techniques such as Wavelet [35], [36], [37], [38], and [39], Geometric features such as corners [40], [41], Saliency map [42], [43], and Frequency domain [44], [45], [46], [47], [48], [49], [50] were widely used. The strengths and weaknesses of the mentioned methods are beyond the scope of this paper, a sufficient discussion is found in [51], and [52].…”

Section: Saliency Identificationmentioning

confidence: 99%

“…As it was discussed earlier, the irregularity shall be measured in terms of uniformity which is represented by the expected value and variation which is represented by the variance or the standard deviation. The irregularity measure can be expressed as given in equation ( 3) [52], [51]. The expected value (the mean) and the variation value (the variance or the standard deviation) 2 or are calculated for the entire image, thus the irregularity mapping ( ) can be extracted as a function of the expected value and the variation value as given in equation (3).…”

Section: Abnormality Identificationmentioning

confidence: 99%

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Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Al-Azawi¹

2021

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Due to the great development in the field of digital image processing, it has become an integral and important part of computer-aided diagnosis systems. Magnetic resonance image (MRI) processing is one of the hot fields that attracted researchers due to its role in improving and speeding up diagnosis. This work presents a new method for identifying abnormal regions in brain MR images using saliency recognition techniques. This is because tumour regions or abnormal regions share with saliency the fact that these regions differ from the rest of the image in terms of size, luminance, and texture. In this paper, we use a saliency extraction method that uses the irregularity of the regions in its definition, as this method depends on the basis that the salient areas in the image are rare and different from the rest of the image, which is what we need to determine the tumour area. The proposed algorithm is applied to a standard dataset and the obtained result showed a high level of accuracy, where the precision, recall, F-measure, and accuracy averages reached 91.8%, 96.2%, 84.5%, and 96% respectively. The results were discussed thoroughly, and the limitations were identified and discussed as well.

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Section: Saliency Identificationmentioning

confidence: 99%

Section: Abnormality Identificationmentioning

confidence: 99%

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Al-Azawi¹

2021

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“…Salient object detection (SOD) aims to detect the salient attention-grabbing objects in a scene and segment the whole objects [1]. It plays a key role in many computer vision tasks, such as image segmentation [2,3], object tracking [4][5][6], image retrieval [7,8], and content-aware image editing [9,10]. With the development of light-field imaging technology, light-field SOD has also received increasing attention [11].…”

Section: Introductionmentioning

confidence: 99%

High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

et al. 2022

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The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional neural network to accurately detect salient objects, by digging effective edge information from light-field data. In particular, our method is divided into four steps. Firstly, the network extracts multi-level saliency features from light-field data. Secondly, edge features are extracted from low-level saliency features and optimized by ground-truth guidance. Then, to sufficiently leverage high-level saliency features and edge features, the network hierarchically fuses them in a complementary manner. Finally, spatial correlations between different levels of fused features are considered to detect salient objects. Our method can accurately locate salient objects with exquisite edge details, by extracting clear edge information and accurate saliency information and fully fusing them. We conduct extensive evaluations on three widely used benchmark datasets. The experimental results demonstrate the effectiveness of our method, and it is superior to eight state-of-the-art methods.

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“…These regions usually contain important information in the scene and can improve the results obtained in image retrieval processes based on their contents. Image saliency has many applications in various fields such as machine vision applications, including image compression, image retrieval [5], object recognition, object segmentation [48,68], medical applications such as in tumour identification [49], and marketing and consumer behaviour applications [8].…”

Section: Introductionmentioning

confidence: 99%

Two-stage salient object identification and segmentation based on irregularity

Al-Azawi

Yang

Istance

2021

Multimed Tools Appl

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Saliency extraction is a technique inspired by the human approach in processing a selected portion of the visual information received. This feature in the human visual system helps reduce the processing the brain needs to extract important information and neglect general and unimportant information. This paper presents a novel approach to identifying the saliency of regions in a scene from which objects likely to be salient can be extracted. The proposed approach uses two stages, namely, local saliency identification (LSI) and global saliency identification (GSI) and uses irregularity as the saliency measure in both stages. Local saliency uses the structure of the object to determine saliency while global saliency identifies the saliency of the region based on the contrast in relation to the entire background. An object is considered to be salient if it satisfies both local and global criteria. In this work, the key challenges and limitations of existing methods, such as the sensitivity to texture and noise, the need to manually define certain parameters, and the need to have pre-knowledge of the nature of the image, were considered and appropriate solutions have been suggested. The proposed algorithm was tested on a set of 1000 images selected from MSRA saliency identification standard dataset and benchmarked with state-of-theart approaches. The results obtained showed very good efficiency and this is evident from the evaluation values obtained from the used evaluation method, e. g. the value of the F-measure, reached 96.5 per cent in some cases. The limitation of the approach was with complex objects which themselves comprising more than one important region such as an image of a person. This will be discussed thoroughly in the result section.

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Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval

Cited by 3 publications

References 32 publications

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

Saliency-Based Brain Abnormality Identification in Magnetic Resonance Images

High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

Two-stage salient object identification and segmentation based on irregularity

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