Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook

Tian, Xiaolin; Jiao, Licheng; Liu, Xianlong; Zhang, Xiaohua

doi:10.1016/j.image.2014.01.010

Cited by 52 publications

(45 citation statements)

References 38 publications

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“…The retrieval results are compared with the state-of-art image retrieval methods, including the methods of Efficient content-based image retrieval using multiple support vector machines ensemble (EMSVM) [51], Simplicity [22], CLUE [23], patch based histogram of oriented gradients-local binary pattern ( Patch based HOG-LBP) [52], and Edge orientation difference histogram and color-SIFT (EODH and Color-SIFT) [53]. The reason of our choice for comparison with these techniques is that: these systems have reported their results on the common denomination of the ten semantic categories of Corel dataset as described earlier.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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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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“…According to Liu et al [11], the spatial information among local features carries significant information for content verification. A rotation and scale-invariant edge orientation difference histogram (EODH) descriptor are proposed by Tian et al [28]. The steerable filter and vector sum are applied to obtain the main orientation of pixels.…”

Section: Related Workmentioning

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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Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook

Cited by 52 publications

References 38 publications

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

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

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