PPD: A Robust Low-computation Local Descriptor for Mobile Image Retrieval

Liu, Congxin

doi:10.3837/tiis.2010.06.007

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…The relative performance among different descriptors is consistent with different feature detectors [13]. Since Hessian-affine detector can detect blob-like points, which are less likely at the positions of depth-difference pixel points and favor local planarity and smoothness assumption [12], it is selected in our experiments.…”

Section: Region Detectormentioning

confidence: 63%

“…There are two major approaches to construct rotation-invariant descriptors. One approach is to rotate the normalized region to align a reference orientation [7,9,10,12,15] and then the feature descriptor is built up. The other is to directly design rotation-invariant descriptor [13,14,16].…”

Section: Related Workmentioning

confidence: 99%

“…Instead of computing the histogram in the square grid, GLOH is calculated along log-polar location grids and the dimension of descriptor is reduced by PCA. PPD descriptor [12] reduces the complexity of standard SIFT and improves its discriminability. PPD adopts region-wise gradient statistics in phase space and also saves interpolation normally used by common SIFT.…”

Section: Related Workmentioning

confidence: 99%

“…One of the popular descriptors is SIFT [7]. Inspired by the discriminability and robustness of the SIFT, lots of variants have been proposed, such as GLOH (gradient location and orientation histogram) [8], SURF [9], PCA-SIFT (principal component analysis-SIFT) [10], DAISY [11] and PPD (phase-space partition based descriptor) [12]. According to the study results of [8], SIFT and GLOH have been demonstrated to be superior to others used in literature on a number of measures.…”

Section: Introductionmentioning

confidence: 99%

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MEGH: A New Affine Invariant Descriptor

2013

KSII TIIS

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An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.

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Section: Region Detectormentioning

confidence: 63%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MEGH: A New Affine Invariant Descriptor

2013

KSII TIIS

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“…In this regard, LPB does not capture the details of microstructures and it is sensitive to image noise. (Karanwal, 2022) Extracting and classifying local image features is a fundamental challenge in many applications (Liu, Yang, & Huang, 2011) and different approaches have been used to describe local image patterns. Some of the descriptors are Shape Context (Salve & Jondhale, 2010), spin image (Lazebnik, Schmid, & Ponce, 2003), complex filters (Schaffalitzky & Zisserman, 2002), steerable filters (Freeman & Adelson, 1991), moment invariants (Van Gool, Moons, & Ungureanu, 1996), SIFT (Lowe, 2004), and the differential invariants (Koenderink & Van Doorn, 1987).…”

Section: Robust Lbb (Rlbp)mentioning

confidence: 99%

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

Appati,

Wunake

2024

Preprint

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The ability to recognize emotional expressions from faces has become an essential component of human-computer interaction (HCI). Recently Oriented FAST and rotated BRIEF (ORB) and Local Binary Patterns (LBP) was used to overcome the limitations of DNN excessive hardware specifications requirements, considering the low hardware specifications used in real-world scenarios. There still exists drawbacks with LBP and ORB, in that LBP is not as resistant to image noise. LBP descriptors are invariant to changing lighting conditions and partial occlusion. Also, when a fixed threshold is utilized under challenging lighting conditions, the ORB algorithm is constrained by its incapacity to extract feature points. We propose a Multi Feature Fusion For Facial Expression Recognition using the algorithms Scale Invariant Feature Transform (SIFT), Histogram Oriented Gradient (HOG), ORB, and LBP. This study proposes a combinatorial blending of least three of these algorithms by looking at the merits of one over the other, also to obtain a novel technique out of the combinatorial schemes, and still obtain better performance of the recognition rates. The proposed method was evaluated on the Extended Cohn Kanade (CK+) and Japanese Famele Facial Expression (JAFFE), and the 2013 Facial Expression Recognition (FER2013) datasets. Based on the merits of our proposed feature extraction schemes, this study explored their respective feature extractions to obtain their individual extracted features from the descriptors. The individual features were then fused together to obtain our multi fused feature, the fused features were then passed onto the classifier for training of our models and image recognitions tasks. This study showed that the proposed algorithm performed well compared to existing state of the art.

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An Image Retrieving Scheme Using Salient Features and Annotation Watermarking

Wang¹,

Liu²,

Syu³

et al. 2014

KSII TIIS

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Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

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PPD: A Robust Low-computation Local Descriptor for Mobile Image Retrieval

Cited by 3 publications

References 21 publications

MEGH: A New Affine Invariant Descriptor

MEGH: A New Affine Invariant Descriptor

A Multi Feature Fusion Search for Facial Expression Recognition Based on Classical Extraction Methods

An Image Retrieving Scheme Using Salient Features and Annotation Watermarking

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