CLRF: Compressed Local Retinal Features for image description

Kannan, Rajkumar; Ghinea, Gheorghiţă; Kannaiyan, Suresh; Swaminathan, Sridhar

doi:10.1109/icapr.2015.7050672

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…The CSLBP and OCLBP are the dimensionality reduced versions of LBP operator that achieved competitive performance than the SIFT descriptor. Recently, the CLRF [7] has achieved better image matching accuracy than the SIFT descriptor in image matching even though it has less dimensional features than the SIFT features.…”

Section: Image Region Descriptorsmentioning

confidence: 99%

“…Hence, the extraction of local features that are invariant to geometric and photometric changes is necessary. Use of local features such as Scale Invariant Feature Transform (SIFT) [6], Compressed Local Retinal Features (CLRF) [7], Speeded up Robust Features (SURF) [8] and Multisupport Region Order-Based Gradient Histogram (MROGH) [9] can help to overcome from these challenges. Recently we proposed the CLRF descriptor [7] utilizing log polar transformation and two dimensional Discrete Wavelet Transformation (2D DWT) and proved that the CLRF is very competitive to the state-ofthe-art descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the previous methods [1, 3,10,11], the proposed approach uses CLRF descriptor [7] to build local features. The CLRF descriptor has some advantages over other descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…It uses log polar transformation (LPT) that sufficiently preserves interest regions structural information and also has the invariance property to translation and rotation [12]. The CLRF descriptor uses the 2D DWT after applying LPT where the usage of 2D DWT makes the local features very compact and discriminative [7].…”

Section: Introductionmentioning

confidence: 99%

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Image Category Recognition using Bag of Visual Words Representation

Kannaiyan¹,

Kannan²,

Ghinea

2016

TMLAI

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Image category recognition is one of the challenging tasks due to difference in image background, illumination, scale, clutter, rotation, etc. Bag-of-Visual-Words (BoVW) model is considered as the standard approach for image categorization. The performance of the BoVW is mainly depend on local features extracted from images. In this paper, a novel BoVW representation approach utilizing Compressed Local Retinal Features (CLRF) for image categorization is proposed. The CLRF uses interest point regions from images and transform them to log polar form. Then two dimensional Discrete Wavelet Transformation (2D DWT) is applied to compress the log polar form and the resultant are considered as features for the interest regions. These features are further used to build a visual vocabulary using k-means clustering algorithm. Then this visual vocabulary is used to form a histogram representation of each image where the images are further classified using Support Vector Machines (SVM) classifier. The performance of the proposed BoVW framework is evaluated using SIMPLIcity and butterflies datasets. The experimental results show that the proposed BoVW approach that uses CLRF is very competitive to the state-of-the-art methods.

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Section: Image Region Descriptorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In contrast to the previous methods [1, 3,10,11], the proposed approach uses CLRF descriptor [7] to build local features. The CLRF descriptor has some advantages over other descriptors.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Category Recognition using Bag of Visual Words Representation

Kannaiyan¹,

Kannan²,

Ghinea

2016

TMLAI

Self Cite

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“…It uses both local and overall intensity ordinal information of the local patch [16], where it produces a highly discriminative descriptor. The CLRF descriptor [28] was proposed using log polar transformation and 2D discrete wavelet transformation to produce a compact descriptor. Most of the aforementioned descriptors used only single interest region around each interest point for description.…”

Section: Image Region Descriptorsmentioning

confidence: 99%

Local Intensity Ordering based Binary Patterns for Image Region Description

Kannan¹,

Kannaiyan²

2017

AIVP

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Local image region description is a fundamental task for image feature matching in the field of Computer Vision. A good image region descriptor should have the ability to discriminate image features even though the images differ due to photometric variations and geometric transformations. Over these years, many local region descriptors have been proposed to tackle the aforementioned challenges. Achieving rotation invariance in keypoint description is considered one of the main challenges in local region description and matching. Previous approaches proposed to tackle rotation variations depend on unreliable dominant orientation estimation. In this paper, two novel local image region descriptors called Local Intensity Order-based Center Symmetric Local Binary Patterns (LIOCSLBP) and Local Intensity Order-based Orthogonally Combined Local Binary Patterns (LIOOCLBP) are proposed to build rotation invariant local region descriptions. The rotation invariance characteristic of the proposed binary pattern-based local region description is achieved by applying a simple and efficient mechanism called Local Intensity Ordering (LIO). The proposed descriptors use double interest regions for each interest point to improve feature discrimination. In order to further improve the feature discrimination ability RGBLIOCSLBP, RGBLIOOCLBP, HSVLIOCSLBP and HSVLIOOCLBP are also proposed exploiting RGB and HSV color models. Extensive experiments are conducted to evaluate the performance of the proposed descriptors on standard benchmark datasets for image matching, object recognition and scene recognition against the state-of-the-art descriptors. The experimental results show that the proposed descriptors are highly competitive to several stateof-the-art local region descriptors where the proposed descriptors outperformed the comparative approaches in many cases.

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blur invariant feature descriptor using multidirectional integral projection

Lee

Park

2016

ETRI J

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Feature detection and description are key ingredients of common image processing and computer vision applications. Most existing algorithms focus on robust feature matching under challenging conditions, such as inplane rotations and scale changes. Consequently, they usually fail when the scene is blurred by camera shake or an object's motion. To solve this problem, we propose a new feature description algorithm that is robust to image blur and significantly improves the feature matching performance. The proposed algorithm builds a feature descriptor by considering the integral projection along four angular directions (0°, 45°, 90°, and 135°) and by combining four projection vectors into a single highdimensional vector. Intensive experiment shows that the proposed descriptor outperforms existing descriptors for different types of blur caused by linear motion, nonlinear motion, and defocus. Furthermore, the proposed descriptor is robust to intensity changes and image rotation.

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CLRF: Compressed Local Retinal Features for image description

Cited by 3 publications

References 22 publications

Image Category Recognition using Bag of Visual Words Representation

Image Category Recognition using Bag of Visual Words Representation

Local Intensity Ordering based Binary Patterns for Image Region Description

blur invariant feature descriptor using multidirectional integral projection

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