scispace - formally typeset
Journal ArticleDOI

Retrieval effectiveness of image search engines

Aabid Hussain, +3 more
- 04 Feb 2019 - 
- Vol. 37, Iss: 1, pp 173-184
Reads0
Chats0
TLDR
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 and provides an insight into the effectiveness of the three ISEs.
Abstract
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.,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.,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.,The study only takes into consideration basic image search feature, i.e. text-based search.,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.,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.

read more

Citations
More filters
Journal ArticleDOI

Researching visual semiotics online

TL;DR: In this article, an integrated, reflexive, Peircean account of two case studies featuring research projects focused on visual data drawn primarily from sources online, relying heavily on Google Image Search as a data collection tool.
Journal ArticleDOI

System Design of Cloud Search Engine Based on Rich Text Content

TL;DR: The experimental results show that the design system has high recall rate, high throughput, and the construction time of each data item index in different files is short, which improves the search efficiency and search accuracy.
Book ChapterDOI

Cross-Model Retrieval Via Automatic Medical Image Diagnosis Generation

TL;DR: The purpose of this research is to present biomedical information retrieval system in order to know more about their strengths and weakness and propose an approach that tries to resolve some gaps and gives some solution to the existing systems and engine retrieval by giving an insight into the images captioning benefit in cross-modality retrieval.
Journal ArticleDOI

Interactive Search on the Web: The Story So Far

TL;DR: An overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories are provided.
Journal ArticleDOI

Three approaches to measuring recall on the Web: a systematic review

TL;DR: A review on the use of the recall metric for evaluating information retrieval systems, especially search engines, and it is better to use the third approach in recall measurement.
References
More filters
Book

Introduction to Information Retrieval

TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Journal ArticleDOI

Real life, real users, and real needs: a study and analysis of user queries on the web

TL;DR: A failure analysis was conducted, identifying trends among user mistakes, and a summary of findings and a discussion of the implications of these findings were concluded.
Journal ArticleDOI

Analyzing the Subject of a Picture: A Theoretical Approach

TL;DR: A theoretical basis for identifying and classifying the kinds of subjects a picture may have is suggested, using previously developed principles of cataloging and classification, and concepts taken from the philosophy of art, from meaning in language, and from visual perception are suggested.
Book

Test Collection Based Evaluation of Information Retrieval Systems

TL;DR: This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.
Journal ArticleDOI

Finding information on the World Wide Web: the retrieval effectiveness of search engines

TL;DR: Traditional information retrieval measures of recall and precision at varying numbers of retrieved documents are calculated and used as the bases for statistical comparisons of retrieval effectiveness among the eight search engines.
Related Papers (5)