scholarly journals Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Samina Bilquees ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ali Javed ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Feature Representation ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Local Contrast ◽  
Query Image ◽  
Gradient Orientation ◽  
Local Gradient

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.

A Content Based Image Retrieval Model Using Feature Space Dividing

2014 ◽  
Vol 596 ◽  
pp. 388-393
Author(s):  
Guan Huang
Keyword(s):  
Image Retrieval ◽  
Fractional Order ◽  
Feature Space ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Time Cost ◽  
Retrieval Model ◽  
Matching Accuracy ◽  
Query Image ◽  
Proposed Model

This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

Content-Based Image Retrieval Using Edge and Gradient Orientation Features of an Object in an Image From Database

2016 ◽  
Vol 25 (3) ◽  
pp. 441-454 ◽  
Author(s):  
H. Kavitha ◽  
M.V. Sudhamani
Keyword(s):  
Image Retrieval ◽  
Experimental Result ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Data Set ◽  
Mode Decomposition ◽  
Gradient Orientation ◽  
Bidimensional Empirical Mode Decomposition ◽  
Edge Features ◽  
The Individual

AbstractIn this work, we present a combination of edge feature and distribution of the gradient orientation of an object technique for content-based image retrieval (CBIR). First, the bidimensional empirical mode decomposition (BEMD) technique is employed to get the edge features of an image. Later, the information about the gradient orientation is obtained by the histogram of oriented gradient (HOG) descriptor. These two features are extracted from the images and stored in the database for further usage. When the user submits the query image, the features are extracted in same way and compared with the features of the data set images. Based on the similarity, the relevant images have been selected as a resultant set. These images are ranked from higher similarity to lower similarity and displayed on the user interface. The experiments are carried out using the Columbia Object Image Library (COIL-100) dataset. The COIL-100 database is a collection of 7200 color images belonging to 100 various objects, each with 72 different orientations. Our proposed method results are high with precision and recall values of 93.00 and 77.70, respectively. Taken individually, the precision and recall values for BEMD are 82.25 and 68.54 and for HOG are 85.00, 71.10, respectively. The observation from the experimental result is that the combined method performs better than the individual methods. Experiments are conducted in the presence of noise, and the robustness of the method is verified.

scholarly journals Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Afshan Latif ◽  
Aqsa Rasheed ◽  
Umer Sajid ◽  
Jameel Ahmed ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Image Classification ◽  
Image Representation ◽  
Feature Representation ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Comprehensive Review ◽  
Visual Understanding

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

scholarly journals A comprehensive survey on content based image retrieval system and its application in medical domain

2018 ◽  
Vol 7 (2.31) ◽  
pp. 181
Author(s):  
K Srinivasa Reddy ◽  
R Anandan ◽  
K Kalaivani ◽  
P Swaminathan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Visual Information ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Radiology Department ◽  
Query Image ◽  
Feature Extraction Method ◽  
Comprehensive Survey ◽  
Medical Domain

Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.  

scholarly journals Performance Analysis of Distance Metric for Content Based Image Retrieval

2019 ◽  
Vol 8 (6) ◽  
pp. 2215-2218
Keyword(s):  
Image Retrieval ◽  
Distance Measure ◽  
Image Features ◽  
Distance Measures ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Feature Vectors ◽  
City Block Distance ◽  
The City

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

scholarly journals A Study of Distance Metrics in Histogram Based Image Retrieval

2013 ◽  
Vol 4 (3) ◽  
pp. 821-830 ◽  
Author(s):  
Abhijeet Kumar Sinha ◽  
K.K. Shukla
Keyword(s):  
Image Retrieval ◽  
Digital Data ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Color Spaces ◽  
Query Image ◽  
Histogram Intersection ◽  
Exhaustive Study ◽  
Different Color

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

Evaluation of Content Based Image Retrieval Technique Using an Efficient Hybrid Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1885-1888
Author(s):  
M. A. Muthiah ◽  
N. Mathan ◽  
E. Logashanmugam
Keyword(s):  
Image Retrieval ◽  
Hybrid Model ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Shape Features ◽  
Query Image ◽  
Visual Technology ◽  
Retrieval Technique ◽  
Future Work ◽  
Calculated Distance

Due to vast enhancement in the field of visual technology, there are various sets of images. In order to reduce the complexity in retrieval of relevant images CBIR (Content Based Image Retrieval) technique can be used. CBIR using only color feature does not result in required output. So in this paper we introduced the concept of hybrid model which deals with color, texture along with shape features which gives an efficient output. A set of images are used to test the accuracy and the precision of each methods. Using Euclidean distance and Manhattan distance, similarity between query image and all the other images in database are calculated. Then the calculated distance values are arranged in ascending order. Based on this required images are retrieved. Experiment results shows that Hybrid model method had high accuracy and precise output compared to Color Histogram. Future work will be made to add one more feature (shape features) in order to get better results.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

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