A novel adaptive LBP-based descriptor for color image retrieval

Sotoodeh, M.; Moosavi, Mohammad Reza; Boostani, Reza

doi:10.1016/j.eswa.2019.03.020

Cited by 44 publications

(19 citation statements)

References 32 publications

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“…Lately, some works propose to combine colour with other texture or shape descriptors. In this line, Ahmed et al [55] used canny edge histogram combined with discrete wavelets on YCbCr colour images or, more recently, Sotoodeh et al [56] presented two approaches to extract discriminative features for colour image retrieval, based on Radial Mean Local Binary Pattern.…”

Section: B Colour Descriptorsmentioning

confidence: 99%

Colour Neural Descriptors for Instance Retrieval Using CNN Features and Colour Models

et al. 2021

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Image representations in the form of neural activations derived from intermediate layers of deep neural networks are the state-of-the-art descriptors for instance based retrieval. However, the problem that persists consists of how to retrieve identical images as the most relevant ones from a large image or video corpus. In this work, we introduce colour neural descriptors that are made of convolutional neural networks (CNN) features obtained by combining different colour spaces and colour channels. In contrast to previous works, which rely on fine-tuning pre-trained networks, we compute the proposed descriptors based on the activations generated from a pretrained VGG-16 network without fine-tuning. Besides, we take advantage of an object detector to optimize our proposed instance retrieval architecture to generate features at both local and global scales. In addition, we introduce a stride based query expansion technique to retrieve objects from multi-view datasets. Finally, we experimentally proved that the proposed colour neural descriptors, obtain state-of-the-art results in Paris 6K, Revisiting-Paris 6k, INSTRE-M and COIL-100 datasets, with mAPs of 81.70, 82.02, 78.8 and 97.9, respectively.

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Section: B Colour Descriptorsmentioning

confidence: 99%

Colour Neural Descriptors for Instance Retrieval Using CNN Features and Colour Models

et al. 2021

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“…Recently, an effective local descriptive has been developed, where the description vectors are easy to calculate called local binary pattern (LBP) [20], [21]. It was used in many modern applications like face recognizing [22], expression faces analysis [23], [24], retrieve components from images or videos [25]. LBP is a simple and easy method to extract local features from images by giving values to the pixels in sub-image, which represent logic outputs of it's comparison with the center of the sub-image.…”

Section: Introductionmentioning

confidence: 99%

Local information pattern descriptor for corneal diseases diagnosis

Jameel

Aydin

Ghaeb

2021

IJECE

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<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may aﬀect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>

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“…Color images on the internet are increasingly demanded and used for many realistic applications. Researchers have created descriptor images which denote color texture designs as well as LBP operators for gray scale images [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

An Improved Image Retrieval by Using Texture Color Descriptor with Novel Local Textural Patterns

Johari¹,

Gupta²

2020

IJACSA

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This paper proposes a new local descriptor of color, texture known as a Median Binary Pattern for color images (MBPC) and Median Binary Pattern of the Hue (MBPH). These suggested methods are extract discriminative features for the color image retrieval. In the surrounding region of a local window, the suggested descriptor classification uses a plane to a threshold that distinguish two classes of color pixels. The Median Binary Patterns of the hue features are derived in the color space from HIS, called MBPH to maximize the discriminatory power of the proposed MBPC operator. In addition to MBPC, MBPH are fused to extract the MBPC+MBPH resulting in an efficient image recovery method combined with color histogram (CH). The structure of the two suggested MBPC and MBPH descriptors are combined with the other fuzzyfied based color histogram descriptor that formed MBPC+MBPH+FCH to improve the performance of the suggested method. The proposed methods are applied on datasets Wang, Corel-5K, and Corel-10K. Experimental results depicted that results of proposed methods are better than existing method in terms of retrieved accuracy. The significant recognition accuracy obtained from the proposed methods which is 60.1 and 63.9 for Wang dataset, 41.88 and 42.47 for Corel-5K and 32.89 and 33.89 for Corel-10K dataset. This hybrid proposed method greatly deals with different textural patterns as well as able to grasp minute color details.

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A novel adaptive LBP-based descriptor for color image retrieval

Cited by 44 publications

References 32 publications

Colour Neural Descriptors for Instance Retrieval Using CNN Features and Colour Models

Colour Neural Descriptors for Instance Retrieval Using CNN Features and Colour Models

Local information pattern descriptor for corneal diseases diagnosis

An Improved Image Retrieval by Using Texture Color Descriptor with Novel Local Textural Patterns

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