scholarly journals Deep Image Retrieval: Learning Global Representations for Image Search

2016 ◽  
pp. 241-257 ◽  
Author(s):  
Albert Gordo ◽  
Jon Almazán ◽  
Jerome Revaud ◽  
Diane Larlus
Keyword(s):  
Image Retrieval ◽  
Image Search ◽  
Deep Image

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.

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.

Deep image retrieval of large-scale vessels images based on BoW model

2019 ◽  
Vol 79 (13-14) ◽  
pp. 9387-9401 ◽  
Author(s):  
Chi Tian ◽  
Jinfeng Xia ◽  
Ji Tang ◽  
Hui Yin
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Image

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.

scholarly journals A New Ranking based Semantic Hashing Method for Deep Image Retrieval

2019 ◽  
Vol 1229 ◽  
pp. 012004
Author(s):  
Haihua Gong ◽  
Kai Xing ◽  
Zitian Li ◽  
Menghan Zhang ◽  
Chunlin Zhong ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Deep Image

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

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