scholarly journals A NEW APPROACH TO SHAPE-BASED IMAGE RETRIEVAL

2014 ◽  
pp. 99-106
Author(s):  
Mehdi Chehel Amirani ◽  
Zahra Sadeghi Gol ◽  
Ali Asghar Beheshti Shirazi
Keyword(s):  
Image Retrieval ◽  
Curvature Function ◽  
Feature Points ◽  
Retrieval Performance ◽  
New Approach ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Active Research ◽  
Low Dimensional ◽  
Object Indexing

Content-based image retrieval (CBIR) is very active research topic in recent years. This paper introduces a new approach to shape-based image retrieval. At first, feature points are determined at the boundary of the shape as the extremums of a new version of the curvature function and the initial features are calculated at these points. The proposed method utilizes a supervised system for nonlinear combination of initial features for extraction of efficient and low dimensional feature vector for each shape. The retrieval performance of the approach is illustrated using the MPEG-7 shape database. Our experiments show that the proposed method is well suited for object indexing and retrieval in large databases.

scholarly journals Performance Analysis of Color based Image Retrieval

2014 ◽  
Vol 12 (4) ◽  
pp. 3373-3381
Author(s):  
Metty Mustikasari ◽  
Sarifuddin Madenda
Keyword(s):  
Image Retrieval ◽  
System Performance ◽  
Euclidean Distance ◽  
Retrieval System ◽  
Image Database ◽  
Retrieval Performance ◽  
City Block ◽  
Average Rank ◽  
Active Research ◽  
City Block Distance

Recently Content based image retrieval (CBIR) is an active research. This paper proposes a technique to retrieve images based on color feature and evaluate the retrieval system performance. In this retrieval system Euclidean distance and City block distance are used to measure similarity of images. This algorithm is tested by using Corel image database which is provided by James Wang.  The performance of retrieval system is measured in terms of its recall and precision.  The effectiveness of retrieval system is also measured based on Average Rank (AVRR) of all relevant retrieves images and Ideal Average Rank of relevant images (IAVRR). The experimental results show that city block has achieved higher retrieval performance than Euclidean distance.

Linguistic Indexing of Images with Database Mediation

2011 ◽  
pp. 2420-2425
Author(s):  
Emmanuel Udoh
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Image Retrieval ◽  
Statistical Modeling ◽  
Digital Images ◽  
Statistical Error ◽  
Large Databases ◽  
Visual Aid ◽  
Active Research

Computer vision or object recognition complements human or biological vision using techniques from machine learning, statistics, scene reconstruction, indexing and event analysis. Object recognition is an active research area that implements artificial vision in software and hardware. Some application examples are autonomous robots, surveillance, indexing databases of pictures and human computer interaction. This visual aid is beneficial to users, because humans remember information with greater accuracy when it is presented visually than when it originates in writing, speech or in kinesthetic form. Linguistic indexing adds another dimension to computer vision by automatically assigning words or textual descriptions to images. This augments content-based image retrieval (CBIR) that extracts or searches for digital images in large databases. According to Li and Wang (2003), most of the existing CBIR projects are general-purpose image retrieval systems that search images visually similar to a query sketch. Current CBIR systems are incapable of assigning words automatically to images due to the inherent difficulty of recognizing numerous objects at once. This current situation is stimulating several research endeavors that seek to assign text to images, thereby improving image retrieval in large databases. To enhance information processing using object recognition techniques, current research has focused on automatic linguistic indexing of digital images (ALIDI). ALIDI requires a combination of mathematical, statistical, computational, and graphical backgrounds. Many researchers have focused on various aspects of linguistic processing such as CBIR (Ghosal, Ircing, & Khudanpur, 2005; Iqbal & Aggarwal, 2002, Wang, 2001) machine learning techniques (Iqbal & Aggarwal, 2002), digital library (Witen & Bainbridge, 2003) and statistical modeling (Li, Gray, & Olsen, 20004, Li & Wang, 2003). A growing approach is the utilization of statistical models as demonstrated by Li and Wang (2003). It entails building databases of images to be used for supervised learning. A trained system is used to recognize and identify new images with statistical error margin. This statistical modeling approach uses a hidden Markov model to extract representative information about any category of images analyzed. However, in using computer to recognize images with textual description, some of the researchers employ solely text-based approaches. In this article, the focus is on the computational and graphical aspects of ALIDI in a system that uses Web-based access in order to enable wider usage (Ntoulas, Chao, & Cho, 2005). This system uses image composition (primary hue and saturation) in the linguistic indexing of digital images or pictures.

scholarly journals Region of interest-based image retrieval techniques: a review

2020 ◽  
Vol 9 (3) ◽  
pp. 520
Author(s):  
Mardhiyah Md Jan ◽  
Nasharuddin Zainal ◽  
Shahrizan Jamaludin
Keyword(s):  
Image Retrieval ◽  
Region Of Interest ◽  
Reference Source ◽  
Global Features ◽  
Retrieval Systems ◽  
Large Databases ◽  
Indexing And Retrieval ◽  
Image Retrieval System ◽  
Specific Interests ◽  
Comprehensive Reference

<span lang="EN-US">This paper presents a review of the region of interest-based (ROI) image retrieval techniques. In this study, the techniques, the performance evaluation parameters, and databases used in image retrieval process are being reviewed. A part of an image that is considered important or a selected certain area of the image is what defines a region of interest. Retrieval performance in large databases can be improved with the application of content-based image retrieval systems which deals with the extraction of global and region features of images. The capability of reflecting users' specific interests with greater accuracy has shown to be more effective when using region-based features compared to global features. Segmentation, feature extraction, indexing, and retrieval of an image are the tasks required in retrieving images that contain similar regions as specified in a query. The idea of the region of interest-based image retrieval concepts is presented in this paper and it is expected to accommodate researchers that are working in the region-based image retrieval system field. This paper reviews the work of image retrieval researchers in the span of twenty years. The main goal of this paper is to provide a comprehensive reference source for scholars involved in image retrieval based on ROI.</span>

scholarly journals Sketch Based Image Retrieval in Large Databases using Edge Features

2020 ◽  
Vol 8 (5) ◽  
pp. 4401-4405
Keyword(s):  
Image Retrieval ◽  
Retrieval Performance ◽  
Efficient Approach ◽  
Overlapping Grids ◽  
Large Databases ◽  
Similar Images ◽  
Edge Features

Sketch-based image retrieval (SBIR) presents better flexibility in expressing the query as sketch for retrieval of images as opposed to text based retrieval. Using a sketch, it is easier to express the orientation and pose of the objects for image retrieval from the database. We propose an efficient approach for SBIR from large databases based on hand awn rough sketch. In the proposed method, images are synthesized to yield a binary sketch that is processed in similar way to user drawn sketch. Edge features are extracted by overlaying the sketch with non-overlapping and overlapping grids, respectively. The most similar images to the query are then retrieved from the database using weighted based similarity approach. Experiments are performed on flickr15k dataset yielding excellent retrieval performance in comparison to the methods available in the literature.

scholarly journals The discovery of biological subphenotypes in ARDS: a novel approach to targeted medicine?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Karin Wildi ◽  
Samantha Livingstone ◽  
Chiara Palmieri ◽  
Gianluigi LiBassi ◽  
Jacky Suen ◽  
...  
Keyword(s):  
Age Groups ◽  
Geographical Location ◽  
High Morbidity ◽  
Shock State ◽  
Pharmacological Agents ◽  
New Approach ◽  
Novel Approach ◽  
Active Research ◽  
Targeted Medicine ◽  
Core Features

AbstractThe acute respiratory distress syndrome (ARDS) is a severe lung disorder with a high morbidity and mortality which affects all age groups. Despite active research with intense, ongoing attempts in developing pharmacological agents to treat ARDS, its mortality rate remains unaltered high and treatment is still only supportive. Over the years, there have been many attempts to identify meaningful subgroups likely to react differently to treatment among the heterogenous ARDS population, most of them unsuccessful. Only recently, analysis of large ARDS cohorts from randomized controlled trials have identified the presence of distinct biological subphenotypes among ARDS patients: a hypoinflammatory (or uninflamed; named P1) and a hyperinflammatory (or reactive; named P2) subphenotype have been proposed and corroborated with existing retrospective data. The hyperinflammatory subphenotyope was clearly associated with shock state, metabolic acidosis, and worse clinical outcomes. Core features of the respective subphenotypes were identified consistently in all assessed cohorts, independently of the studied population, the geographical location, the study design, or the analysis method. Additionally and clinically even more relevant treatment efficacies, as assessed retrospectively, appeared to be highly dependent on the respective subphenotype. This discovery launches a promising new approach to targeted medicine in ARDS. Even though it is now widely accepted that each ARDS subphenotype has distinct functional, biological, and mechanistic differences, there are crucial gaps in our knowledge, hindering the translation to bedside application. First of all, the underlying driving biological factors are still largely unknown, and secondly, there is currently no option for fast and easy identification of ARDS subphenotypes. This narrative review aims to summarize the evidence in biological subphenotyping in ARDS and tries to point out the current issues that will need addressing before translation of biological subohenotypes into clinical practice will be possible.

scholarly journals Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

2021 ◽  
Vol 13 (23) ◽  
pp. 4786
Author(s):  
Zhen Wang ◽  
Nannan Wu ◽  
Xiaohan Yang ◽  
Bingqi Yan ◽  
Pingping Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binary Code ◽  
Satellite Observation ◽  
Cross Entropy ◽  
Retrieval Task ◽  
Search Tasks ◽  
Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

Issues and Techniques to Mitigate the Performance Gap in Content-Based Image Retrieval Systems

2011 ◽  
pp. 60-83
Author(s):  
Agma J. M. Traina ◽  
Caetano Traina ◽  
Robson Cordeiro ◽  
Marcela Ribeiro ◽  
Paulo M. Azevedo-Marques
Keyword(s):  
Image Retrieval ◽  
Poor Quality ◽  
Content Based Image Retrieval ◽  
Performance Gap ◽  
Similarity Queries ◽  
Access Methods ◽  
Retrieval Systems ◽  
Computational Support ◽  
Indexing And Retrieval ◽  
Key Aspects

This chapter discusses key aspects concerning the performance of Content-based Image Retrieval (CBIR) systems. The so-called performance gap plays an important role regarding the acceptability of CBIR systems by the users. It provides a timely answer to the actual demand for computational support from CBIR systems that provide similarity queries processing. Focusing on the performance gap, this chapter explains and discusses the main problems currently under investigation: the use of many features to represent images, the lack of appropriate indexing structures to retrieve images and features, deficient query plans employed to execute similarity queries, and the poor quality of results obtained by the CBIR system. We discuss how to overcome these problems, introducing techniques such as how to employ feature selection techniques to beat the “dimensionality curse” and how to use proper access methods to support fast and effective indexing and retrieval of images, stressing the importance of using query optimization approaches.

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