Parallel implementation of local derivative pattern algorithm for fast image retrieval

2015 ◽  
Author(s):  
S. Nisha Chandran ◽  
Durgaprasad Gangodkar ◽  
Ankush Mittal
Keyword(s):  
Image Retrieval ◽  
Parallel Implementation ◽  
Local Derivative ◽  
Local Derivative Pattern

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

scholarly journals Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

2018 ◽  
Vol 8 (6) ◽  
pp. 5457
Author(s):  
Nisha Chandran ◽  
Durgaprasad Gangodkar ◽  
Ankush Mittal
Keyword(s):  
High Resolution ◽  
Image Retrieval ◽  
Performance Optimization ◽  
General Purpose ◽  
Content Based Image Retrieval ◽  
Maximum Speed ◽  
Processing Unit ◽  
Efficient Retrieval ◽  
Local Derivative ◽  
Local Derivative Pattern

<p><span>Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart.</span></p>

Local derivative radial patterns: A new texture descriptor for content-based image retrieval

2017 ◽  
Vol 137 ◽  
pp. 274-286 ◽  
Author(s):  
Sadegh Fadaei ◽  
Rassoul Amirfattahi ◽  
Mohammad Reza Ahmadzadeh
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Texture Descriptor ◽  
Local Derivative
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