Feature integration analysis of bag-of-features model for image retrieval

Yu, Jing; Qin, Zengchang; Wan, Tao; Zhang, Xi

doi:10.1016/j.neucom.2012.08.061

Cited by 147 publications

(94 citation statements)

References 20 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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“…The two new feature integrations are image-based SIFT-LBP and patch-based HOG-LBP. The image-based integration of SIFT-LBP outperforms the state-of-the-art approaches [22]. Zhang et al [27] proposed a rotation invariant image matching system by using combination of SIFT and LBP.…”

Section: Related Workmentioning

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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“…Global image features are extracted from various formats of images by image processing. The features are combined for use in CBIR [2], [3]. CBIR systems for scene images, biomedical images, product images, and so on, have been investigated for applications in fields such as security systems (biometrics), medical management systems, online e-commerce and computer vision (see [1]- [10]).…”

Section: Introductionmentioning

confidence: 99%

Robust Similar Image Retrieval Based on Extracted Object Features

Nishiki

Wada

2017

Journal of Signal Processing

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In this paper, a robust similar image retrieval method using extracted object features is proposed. A local texture feature as well as global contour and color features are extracted from an object image to generate unified robust feature vectors. To show the effectiveness of our method, experimental noisy image retrieval was executed using standard object image databases. The recall rate, precision rate and F-measure obtained by cross-validation were calculated to evaluate the performance of object image retrieval. High-performance image retrieval was achieved compared with the conventional methods without using combined robust features of extracted objects.

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Feature integration analysis of bag-of-features model for image retrieval

Cited by 147 publications

References 20 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

Robust Similar Image Retrieval Based on Extracted Object Features

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