scholarly journals Contextual Image Illustrator: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 1228-1232
Author(s):  
Kanika Sharma
Keyword(s):  
Image Retrieval ◽  
Keyword Extraction ◽  
Unit Testing ◽  
Image Search ◽  
Newspaper Article ◽  
Retrieval Process ◽  
Content Description ◽  
Stop Word ◽  
Ranking Scheme ◽  
Image Ranking

Abstract: Any story or any other literary content is best understood and advertised with the help of pictures. Images are used to arouse reader’s interest and comprehension in the content. The contextual image illustrator will take any content description and will output the ranked images related to that content. The text can be any blog, newspaper article, any story or any other content. The image retrieval process that has been used for this purpose is Text based Image Retrieval, i.e., TBIR. Semantic keywords are extricated from the story; images are looked through an annotated database. Thereafter, an image ranking scheme will determine the relevance of each image. Then the user can choose among the images displayed. A score along with each image will also be displayed representing its relevance to the query. The keywords stemming and stop word removal has been explained in the document. Also, the algorithm that has been designed to determine the score and hence the image’s significance has been calculated. Testing consisting of both unit testing and module testing of the project are explained. Keywords: Keyword Extraction, Image Search, Stemming, Stop word Removal, URL Score, URL Ranking

Evaluation of Light Inspired Optimization Algorithm-Based Image Retrieval

2014 ◽  
Vol 573 ◽  
pp. 529-536
Author(s):  
T. Kanimozhi ◽  
K. Latha
Keyword(s):  
Image Retrieval ◽  
Firefly Algorithm ◽  
Retrieval System ◽  
Computation Time ◽  
Image Databases ◽  
Image Search ◽  
Retrieval Process ◽  
Large Collection ◽  
Image Retrieval System ◽  
Unique Method

Image retrieval system becoming a more popular in all the disciplines of image search. In real-time, interactive image retrieval system has become more accurate, fast and scalable to large collection of image databases. This paper presents a unique method for an image retrieval system based on firefly algorithm, which improve the accuracy and computation time of the image retrieval system. The firefly algorithm is utilized to optimize the image retrieval process via search for nearly optimal combinations between the corresponding features as well as finding out approximate optimized weights for similarities with respect to the features. The proposed method is able to dynamically reflect the user’s intention in the retrieval process by optimizing the objective function. The Efficiency of the proposed method is compared with other existing image retrieval methods through precision and recall. The performance of the method is experimented on the Corel and Caltech database images.

scholarly journals Image Retrieval Using Data Mining Technique

2020 ◽  
pp. 2115-2125
Author(s):  
Sarmad T. Abdul-Samad ◽  
Sawsan Kamal
Keyword(s):  
Image Retrieval ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Retrieval System ◽  
Spatial Information ◽  
Search Space ◽  
Extraction Process ◽  
Retrieval Process ◽  
Data Mining Technique ◽  
Image Retrieval System

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.  

scholarly journals The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

2018 ◽  
Vol 7 (3.1) ◽  
pp. 13
Author(s):  
Raveendra K ◽  
R Vinoth Kanna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Document Image ◽  
Retrieval Process ◽  
Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.  

Nested Partitions Properties for Spatial Content Image Retrieval

2012 ◽  
pp. 240-269
Author(s):  
Dmitry Kinoshenko ◽  
Vladimir Mashtalir ◽  
Vladislav Shlyakhov ◽  
Elena Yegorova
Keyword(s):  
Image Retrieval ◽  
Hierarchical Models ◽  
Search Algorithms ◽  
Content Based Image Retrieval ◽  
Image Search ◽  
Nested Partitions ◽  
Specific Search ◽  
Spatial Content

In this paper, a metric on partitions of arbitrary measurable sets and its special properties for metrical content-based image retrieval based on the ‘spatial’ semantic of images is proposed. This approach considers images represented in the form of nested partitions produced by any segmentations, which are used to express a degree of information refinement or roughening. In doing so, this not only corresponds to rational content control but also ensures creation of specific search algorithms (e.g., invariant to image background) and synthesizes hierarchical models of image search by reducing the number of query and database elements match operations.

Optimized Image Retrieval System in Oracle DBMS

2017 ◽  
Vol 8 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Kaouther Zekri ◽  
Amel Grissa Touzi ◽  
Noureddine Ellouze
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Similarity Criteria ◽  
Search Process ◽  
Textual Information ◽  
Experimental Part ◽  
Content Description ◽  
Image Retrieval System ◽  
Assistance Tool ◽  
Selection Of

In this work, the authors are moving towards the creation of an effective image retrieval system in Oracle DBMS. Several DBMSs have been extensively used to manage the textual information stored with images and CBIR tasks usually rely on specific applications. The separation between the DBMSs and CBIR prevents the optimization of integrated search process based on the connection between the textual and visual content description of image. Moreover, the relevance of image retrieval depends directly on the choice of similarity criteria (color, texture, shape) that can give inaccurate results in case of non-trivial selection of these parameters. The purpose of the authors' approach is to build a CBIR system using advanced and integrated retrieval techniques defined in Oracle DBMS. This approach provides an assistance tool that can guide the user to the appropriate choice of search criteria. The authors present an experimental part that measures the performance of their system, which can help the user to correctly model his query by giving the appropriate retrieval criteria for a database with 800 images.

Retrieval effectiveness of image search engines

2019 ◽  
Vol 37 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Aabid Hussain ◽  
Sumeer Gul ◽  
Tariq Ahmad Shah ◽  
Sheikh Shueb
Keyword(s):  
Image Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Keyword Search ◽  
Image Search ◽  
Image Size ◽  
Test Bed ◽  
Content Type ◽  
Retrieval Effectiveness ◽  
Search Feature

Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.

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