Clustering of Retrieved Images by Integrating Perceptual Signal Features within Keyword-Based Image Search Engines

2009 ◽  
pp. 956-961 ◽  
Author(s):  
Bashar Tahayna ◽  
Mohammed Belkhatir ◽  
Yandan Wang
Keyword(s):  
Search Engines ◽  
Image Search ◽  
Signal Features

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.

Descriptive Text Generation for Image Search Result

2010 ◽  
Vol 171-172 ◽  
pp. 94-97
Author(s):  
Rui Liu ◽  
Ming Hu Jiang
Keyword(s):  
Search Engines ◽  
Auxiliary Information ◽  
Nearest Neighbors ◽  
The Internet ◽  
Image Search ◽  
Text Generation ◽  
Position Information ◽  
Search Result ◽  
Image Caption

The image search engines have been effective tools to find pictures from the Internet. They provide a list of image items in response to a user’s query, and rank the items according to their relevance to the query. An image item is often accompanied with a short descriptive text, which is brief text summaries extracted from the webpage title, content, image caption, or its metadata, to provide auxiliary information about the image. In this paper, we present a new and effective descriptive text generation method by using the idea of summarizing an image’s surrounding text, using text’s position information, and finding an image’s nearest neighbors.

scholarly journals Content-Based Image Retrieval for Multiple Objects Search

2017 ◽  
Vol 17 (2) ◽  
pp. 106-118
Author(s):  
Gábor Szűcs ◽  
Dávid Papp
Keyword(s):  
Search Engines ◽  
Search Task ◽  
State Of The Art ◽  
Image Search ◽  
Multiple Objects ◽  
Search System ◽  
Search Methods ◽  
Complex Queries ◽  
Web Images ◽  
Binary Classifiers

Abstract The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image search system, which is capable of multiple object retrieval using only visual content of the images. We have used the state-of-the-art image processing methods prior to the search, such as Fisher-vector and C-SVC classifier, in order to semantically classify images containing multiple objects. The results of this offline classification are stored for the latter search task. We have elaborated more search methods for combining the results of binary classifiers of objects in images. Our search methods use confidence values of object classifiers and after the evaluation, the best method is selected for thorough analysis. Our solution is compared with the famous web images search engines (Google, Bing and Flickr), and there is a comparison of their Mean Average Precision (MAP) values. It can be concluded that our system reaches the benchmark; moreover, in most cases our method outperforms the others, especially in the cases of queries with many objects.

An Evaluation of Major Image Search Engines on Various Query Topics

2008 ◽  
Author(s):  
Ece Cakir ◽  
Huseyin Bahceci ◽  
Yiltan Bitirim
Keyword(s):  
Search Engines ◽  
Image Search

scholarly journals Linguistic Query Based Quality Evaluation of Selected Image Search Engines

2017 ◽  
Vol 112 ◽  
pp. 1809-1818 ◽  
Author(s):  
Artur Karczmarczyk ◽  
Jarosław Jankowski ◽  
Wojciech Sałabun
Keyword(s):  
Search Engines ◽  
Quality Evaluation ◽  
Image Search

Intuitive and effective interfaces for WWW image search engines

2004 ◽  
Author(s):  
Zhi-Wei Li ◽  
Xing Xie ◽  
Hao Liu ◽  
Xiaoou Tang ◽  
Mingjing Li ◽  
...  
Keyword(s):  
Search Engines ◽  
Image Search

scholarly journals Improvement of natural image search engines results by emotional filtering

2016 ◽  
Vol 3 (6) ◽  
pp. 151164 ◽  
Author(s):  
Patrice Denis ◽  
Vincent Courboulay ◽  
Arnaud Revel ◽  
Syntyche Gbèhounou ◽  
François Lecellier ◽  
...  
Keyword(s):  
Search Engines ◽  
Natural Image ◽  
Image Search
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