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>

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.

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

scholarly journals Feature Extraction and Content Based Image Retrieval for High Resolution Remote Sensing Images

2019 ◽  
Vol 8 (3) ◽  
pp. 8881-8884
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Image Retrieval ◽  
Satellite Images ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Remote Sensing Images ◽  
Image Collection

These are the days where we are very rich in information and poor in data. This is very true in case of image data. Whether it is the case of normal images or satellite images, the image collection is very huge but utilizing those images is of least concern. Extracting features from big images is a very challenging and compute intensive task but if we realize it, it will be very fruitful. CBIR (Content Based Image Retrieval) when used with HRRS (High Resolution Remote Sensing) images will yield with effective data.

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

scholarly journals Machine Learning Paradigm towards Content Based Image Retrieval on High Resolution Satellite Images

2019 ◽  
Vol 9 (2S2) ◽  
pp. 999-1005
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Image Retrieval ◽  
Satellite Images ◽  
Satellite Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Ensemble Model ◽  
Learning Paradigm ◽  
High Resolution Satellite Images

In the current era, content based image retrieval based on pattern recognition and classification using machine learning paradigm is an innovative way. In order to retrieve high resolution satellite images Support Vector Machine (SVM) a machine learning paradigm is helpful for learning process and for pattern recognition and classification; ensemble methods give better machine learning results. In this paper, SVM based on random subspace and boosting ensemble learning is proposed for very high resolution satellite image retrieval. The learned SVM ensemble model is used to identify the images that most similar informative for active learning. A bias-weighting system is developed to direct the ensemble model to pay more attention on the positive examples than the negative ones. The UCMerced land use satellite image dataset is used for experimental work. Accuracy and error rate are found to be precise. The tentative effects illustrate that the proposed model derived enhanced retrieval accurateness at the optimum level as well as significantly more effective than existing approaches. The proposed method can diminish the gap dimensionality and conquer the difficulty. The comparisons are evaluated by using precision and recall measurements. Comparative analysis observed that the retrieval time for a particular image have been reduced and the precision is increased. The primary aim of this paper is to represent the significance of ensemble learning with support vector machine in efficient retrieval of image.

scholarly journals Comparison of query optimization techniques in content based image retrieval (CBIR)

2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Mariam Alharthi ◽  
Fahad Alqurashi
Keyword(s):  
Information Systems ◽  
Image Retrieval ◽  
Query Optimization ◽  
World Wide ◽  
Optimization Techniques ◽  
Content Based Image Retrieval ◽  
Retrieval Process ◽  
Digital Contents ◽  
Efficient Retrieval ◽  
Similar Images

With increasing the popularity of World Wide Web, storing digital contents increases enormously, in that case, it is important to implement convenient information systems which manage the collections of these digital contents efficiently. This paper concentrates on hastening techniques for efficient retrieval of images. Content-Based Image Retrieval (CBIR) systems are used by common approaches. These systems support retrieving similar images depend on content properties (e.g., color, shape, and texture) by retrieving automatically similar images to a pattern or user-defined specification. The CBIR generally used in several applications by applying different techniques in each application which in turns enhance the retrieval process. The paper aims to evaluate some of these applications and compare them to find out the proper methods that return the best results in these CBIR systems.

scholarly journals A Content Based Image Retrieval Model for E-Commerce

2018 ◽  
Vol 7 (``11) ◽  
pp. 24392-24396
Author(s):  
Gibson Kimutai ◽  
Prof. Wilson Cheruiyot ◽  
Dr. Calvins Otieno
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Large Database ◽  
Visual Content ◽  
Color Feature ◽  
Search Results ◽  
Efficient Retrieval ◽  
Database Content

In the last decade, large database of images have grown rapidly. This trend is expected to continue in to the future. Retrieval and querying of these image in efficient way is a challenge in order to access the visual content from large database. Content Based Image Retrieval (CBIR) provides the solution for efficient retrieval of image from these huge image database. Many research efforts have been directed to this area with color feature being the mostly used feature because of its ease of extraction. Although many research efforts have been directed to this area, precision  of majority of the developed models  are still at less than 80%. This is a challenge as it leads to unsatisfying search results. This paper proposes a Content Based Image Retrieval model for E-Commerce.

Sign in / Sign up

Export Citation Format

Share Document