An improved radial basis function neural network for object image retrieval

Montazer, Gholam Ali; Giveki, Davar

doi:10.1016/j.neucom.2015.05.104

Cited by 67 publications

(49 citation statements)

References 45 publications

(62 reference statements)

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“…Table II and Table III The best values of precision and recall are mentioned as bold in Table II and Table III. The MAP of proposed late fusion outperforms state-of-the-art research [22], [26], [34], [32], [41]. …”

Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 81%

“…2 represents a sample of randomly selected images from each class of the Corel-1000. We selected Corel-1000 for the evaluation of proposed framework as it has been recently used to evaluate the performance CBIR research [22], [26], [34], [32], [41]. We varied the codebook size to sort out the best retrieval performance of proposed work.…”

Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 99%

“…The MAP on codebook of each size by using the proposed late fusion is higher than that of SIFT and FREAK. To present a sustainable performance, the best MAP obtained from the proposed late fusion is compared with existing research [22], [26], [34], [32], [41]. Table II and Table III The best values of precision and recall are mentioned as bold in Table II and Table III.…”

Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 99%

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Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation.Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

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Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 81%

Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 99%

Section: B Performance Evaluation Using Corel-1000mentioning

confidence: 99%

See 1 more Smart Citation

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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show abstract

“…The proposed technique is evaluated using image benchmark of the Corel-1K, and recent CBIR techniques [28,[41][42][43][44] are used for the performance comparison of the proposed technique. The image benchmark of the Corel-1K comprises of 1000 images, which are organized into 10 semantic classes.…”

Section: Analysis Of the Evaluation Metrics On The Image Benchmark Ofmentioning

confidence: 99%

“…This shows that increasing the pixel stride decreases the MAP performance and vice versa. In order to present a sustainable performance of the proposed research, the MAP for top-20 retrievals is calculated and compared with recent CBIR techniques [28,[41][42][43][44]. Table 2 and Table 3 are presenting the class-wise comparisons of average precision and recall on the image benchmark of the Corel-1K.…”

Section: Analysis Of the Evaluation Metrics On The Image Benchmark Ofmentioning

confidence: 99%

Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

Mehmood

Gul

Altaf

et al. 2018

J Image Video Proc.

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The storage size of the image and video repositories are growing day by day due to the extensive use of digital image acquisition devices. The position of an object within an image is obtained by analyzing the content-based properties like shape, texture, and color, while compositional properties present the image layout and include the photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that divide each image into nine square areas. According to this rule, salient objects of an image are placed on the intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique, which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis performed on five image collections show an encouraging robustness of the proposed technique that is compared with the recent CBIR techniques.

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A new method for image classification and image retrieval using convolutional neural networks

Giveki

Shakarami²,

Tarrah

et al. 2021

Concurrency and Computation

Self Cite

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This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel‐1k, Corel‐10k, Caltech‐101 object and Scene‐67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state‐of‐the‐art in the field.

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An improved radial basis function neural network for object image retrieval

Cited by 67 publications

References 45 publications

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

A new method for image classification and image retrieval using convolutional neural networks

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