Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

Naghashi, Vahid

doi:10.1016/j.ijleo.2017.11.160

Cited by 40 publications

(17 citation statements)

References 15 publications

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“…The histograms are concatenated for generating a descriptor (double the size of LBP), which is very successful in the application of facial recognition. The basic idea of LTP is to transform the intensity space to order space, where the order of neighbouring pixels is utilized for creating a monotonic change illumination invariant code for every image [20].…”

Section: Binary Robust Invariant Scalable Key Pointsmentioning

confidence: 99%

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Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Prakash¹

2020

IJIES

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Face recognition is an emerging research area in biometric identification systems, which attracted many researchers in pattern recognition and computer vision. Since, the human facial images are high dimensional in nature, so a new optimization algorithm was proposed in this paper to reduce the dimension of facial images. At first, the input facial images were collected from ORL, YALE, and FAce Semantic SEGmentation (FASSEG) databases. Then, feature extraction was then performed by using Local Ternary Pattern (LTP) and Binary Robust Invariant Scalable Key points (BRISK) to extract the features from the face images. In addition, an enhanced fire-fly optimizer was used to reject the irrelevant features or to reduce the dimension of the extracted features. In enhanced fire-fly optimization algorithm, Chi square distance measure was used to find the distance between the fire-flies and also it uses only a limited number of feature values for representing the data that effectively reduces the "curse of dimensionality" concern. Finally, Deep Belief Network (DBN) was used to classify the individual's facial images. The experimental outcome showed that the proposed work improved recognition accuracy up to 7-20% compared to the existing work.

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Section: Binary Robust Invariant Scalable Key Pointsmentioning

confidence: 99%

“…The configuration makes it easy to process the conditional probability distribution, where and are fixed that are mathematically described in the Eqs. (19) and (20).…”

Section: Classificationmentioning

confidence: 99%

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Prakash¹

2020

IJIES

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“…In CBIR systems, after the recovery of indistinguishable images from a huge repository of images, the capability of a particular system can be concluded with respect to many evaluation parameters [36][37]. Precision and Recall are the most well-known evaluation metrics.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Bhardwaj

Pandove

Dahiya

2019

ICST Transactions on Scalable Information Systems

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Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.

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“…It is basically a texture based CBIR system [20]. In [21] different type of image retrieval system was designed using hashing based similarity search. It reduces the feature space and thus reduces the computational time and provides the good performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery

Dhingra

Bansal

2018

ICST Transactions on Scalable Information Systems

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An efficient and novel image retrieval system is framed here, which retrieve images from massive datasets to overcome the constraints of efficiency and retrieval time. Thus to address this issue, an effective indexing technique is proposed on the hybrid system constituted by low level features of the image. Firstly, features are extracted from the combination color moment, LBP and segmentation to form a hybrid feature space. To reduce its dimensional space, principle component analysis is exercised which provide lesser and good quality features. On this space, two expedient indexing techniques are proposed: cluster based and similarity based. The approach that is proposed here is an innovative design of a hybrid content based image retrieval system, as in this framework all the skilled techniques are merged to form a competent and dynamic image retrieval system. Five touchstone datasets are used to test the performance of the system. Extensive experiments are carried out which shows that the system with cluster based indexing technique provides highlighted results as compared to similarity based technique and also surpasses the other latest state of art techniques in terms of precision and retrieval time

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Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

Cited by 40 publications

References 15 publications

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

A Novel & Efficient Fusion Based Image Retrieval Model for Speedy Image Recovery

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