Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

Yu, Lingli; Xia, Xumei; Zhou, Kaijun; Zhao, Lina

doi:10.1049/iet-ipr.2018.5488

Cited by 8 publications

(9 citation statements)

References 46 publications

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“…Techniques RR Affine transformation due to viewing angle and distance variations. Therefore, affine invariant feature extraction [22] 92.50 Local and Holistic features with Neural Networks [23] 93.46 Signal Reconstruction with Neural Networks [24] 93.89 HAAR-like features and eigenfaces [25] 93.91 Proposed Technique (Average) 93.95 Proposed Technique (Best) 94.12…”

Section: Table 2 Comparing Proposed Technique With State Of Artmentioning

confidence: 99%

Face recognition using selected topographical features

Naji

Salman

Fadhil

2020

IJECE

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This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier

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Section: Table 2 Comparing Proposed Technique With State Of Artmentioning

confidence: 99%

Face recognition using selected topographical features

Naji

Salman

Fadhil

2020

IJECE

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“…Further shape descriptors with affine invariance support is focused by many authors [24,[27][28][29][30][31]. Recently, owing to the availability of huge computational resources, many authors have used the feature fusion and multi-scale representation of shapes, which consequently increase the overall performances [2,10,17,[32][33][34][35][36]. The multi-resolution rotation, scaling, translation (RST) invariant shape descriptor designed by Attalla and Siy is based on polygon approximation [20].…”

Section: Related Workmentioning

confidence: 99%

“…Further shape descriptors with affine invariance support is focused by many authors [24, 27–31]. Recently, owing to the availability of huge computational resources, many authors have used the feature fusion and multi‐scale representation of shapes, which consequently increase the overall performances [2, 10, 17, 32–36].…”

Section: Related Workmentioning

confidence: 99%

“…In recent reports, the feature fusion and multi-scale shape description methods have widely been used as they capture the maximum information of shapes [2,4,10,17,[32][33][34][35][36]]. An affine invariant feature fusion method, introduced by Yu et al, performs the parallel extraction of geometric features with a twostage double biologically inspired transformation (DBIT) features from the images [17]. This method uses the Pearson correlation distance as a weighted fusion strategy to combine the geometric and DBIT features for object recognition tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The conventional shape matching and retrieval applications perform analysis methods of equal distance normalisation (EDN) on the shape contours [4,[8][9][10][11][12][13]. In addition with EDN, the boundary can also be normalised by accounting the enclosed area of the shapes to capture the complete information about the shape [6,[14][15][16][17]. The shape descriptors proposed in this work exploit the enclosed area based boundary normalisation called object area normalisation (OAN), which can further be implemented by using triangle or sector area [6,14] approaches.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

Paramarthalingam¹,

Thankanadar²

2020

IET Image Processing

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Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Novel Algorithms for 2DRSP and 2DISP

Rao

Luo

2022

Intelligent Algorithms for Packing and Cutting Problem

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Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

Cited by 8 publications

References 46 publications

Face recognition using selected topographical features

Face recognition using selected topographical features

Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

Novel Algorithms for 2DRSP and 2DISP

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