Learning from search engine and human supervision for web image search

2011 ◽  
Author(s):  
Linjun Yang ◽  
Alan Hanjalic
Keyword(s):  
Search Engine ◽  
Image Search

Virage image search engine: an open framework for image management

1996 ◽  
Author(s):  
Jeffrey R. Bach ◽  
Charles Fuller ◽  
Amarnath Gupta ◽  
Arun Hampapur ◽  
Bradley Horowitz ◽  
...  
Keyword(s):  
Search Engine ◽  
Image Management ◽  
Image Search ◽  
Open Framework

MISE: The MediaSys Image Search Engine

2000 ◽  
pp. 993-1002
Author(s):  
Frédéric Andrés ◽  
Nicolas Dessaigne ◽  
José Martinez ◽  
Noureddine Mouaddib ◽  
Kinji Ono ◽  
...  
Keyword(s):  
Search Engine ◽  
Image Search

Content Based Search Engine for Historical Calligraphy Images

2014 ◽  
Vol 10 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Xiafen Zhang ◽  
Vijayan Sugumaran
Keyword(s):  
Search Engine ◽  
High Dimensional ◽  
Image Search ◽  
Shape Features ◽  
Document Images ◽  
Search Query ◽  
Image Content ◽  
Content Search ◽  
Image Format ◽  
Potential Use

Paper collections of historical calligraphy objects in Libraries and museums are scanned into document images to serve the academic society. However, these digitized collections are in image format, lacking the technology to search by image content. This paper proposes a search engine for searching calligraphy image content. First, 2503 page images are segmented into characters and components. Second, characters are interactively labeled and features are extracted to build a calligraphy database. When an image search query is submitted, coarse features are first extracted and used to prune the long list of calligraphy characters into a shorter list. Then fine shape features are employed to determine the most similar characters. iDistance and NB-Tree are used to create the high dimensional index. The efficiency of the algorithm has been demonstrated through experiments with 110,737 individual calligraphic character images. This research provides a demonstration of the potential use of calligraphy content search on the web.

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.

Image search engine feature analysis

2001 ◽  
Vol 25 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Ibrahim Hassan ◽  
Jin Zhang
Keyword(s):  
Search Engine ◽  
Feature Analysis ◽  
Image Search

Towards more Efficient for Web Image Search Engine using Dominant Meaning Technique

2017 ◽  
Vol 54 (2) ◽  
pp. 91-96
Author(s):  
Yasser Ibrahim ◽  
Mohammed Abdel Razek ◽  
Kamal A ElDahshan
Keyword(s):  
Search Engine ◽  
Image Search ◽  
Dominant Meaning
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