Image matching based on LBP and SIFT descriptor

Kabbai, Leila; Azaza, Aymen; Abdellaoui, Mehrez; Douik, Ali

doi:10.1109/ssd.2015.7348116

“…The regions of LBP descriptors are calculated by using SIFT detector. Kabbai et al [24] proposed a new approach to extract invariant features from the regions of interest. The uniform pattern is applied to the LBP and Center Symmetric Local Binary Pattern (CSLBP) for a robust image matching application.…”

Section: Related Workmentioning

confidence: 99%

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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“…Thus, for each central pixel, a binary number can be generated by concatenating all of these binary bits in a clockwise direction. The generated binary number’s decimal value is used to replace the central pixel value ( Moujahid & Dornaika, 2019 ; Kabbai et al, 2015 ; Ahonen, Hadid & Pietikainen, 2006 ; Ahonen, Hadid & Pietikäinen, 2004 ). A binary pattern representing texture characteristics will be generated by the threshold process as in Fig.…”

Section: Kvr Stagesmentioning

confidence: 99%

“…Lowe proposed the SIFT algorithm to extract the most stable interest points of an image and construct a vector descriptor using the Difference of Gaussian (DoG). The algorithm is described in the following steps Kabbai et al (2015) : 1. Scale-Space Extrema Detection This step is responsible for constructing pyramids at various scales of the Gaussian function and interest points are extracted with invariant to scale and orientation.…”

Section: Kvr Stagesmentioning

confidence: 99%

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Nader

¹

,

El-Gamal

²

,

El–Sappagh

³

et al. 2021

PeerJ Computer Science

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Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

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“…The regions of LBP descriptors are calculated by using SIFT detector. Kabbai et al [28] proposed a new approach to extract invariant features from the regions of interest. The uniform pattern is applied to the LBP and Center Symmetric Local Binary Pattern (CSLBP) for a robust image matching application.…”

Section: Related Workmentioning

confidence: 99%

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

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

View full text Add to dashboard Cite

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.

show abstract

Image matching based on LBP and SIFT descriptor

Cited by 22 publications

References 17 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

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

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

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