Open Access   Article Go Back

Improving Visual Search Results

Saichethan Reddy1

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-12 , Page no. 169-171, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i12.169171

Online published on Dec 31, 2018

Copyright © Saichethan Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Saichethan Reddy, “Improving Visual Search Results,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.169-171, 2018.

MLA Style Citation: Saichethan Reddy "Improving Visual Search Results." International Journal of Computer Sciences and Engineering 6.12 (2018): 169-171.

APA Style Citation: Saichethan Reddy, (2018). Improving Visual Search Results. International Journal of Computer Sciences and Engineering, 6(12), 169-171.

BibTex Style Citation:
@article{Reddy_2018,
author = {Saichethan Reddy},
title = {Improving Visual Search Results},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {6},
Issue = {12},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {169-171},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3311},
doi = {https://doi.org/10.26438/ijcse/v6i12.169171}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i12.169171}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3311
TI - Improving Visual Search Results
T2 - International Journal of Computer Sciences and Engineering
AU - Saichethan Reddy
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 169-171
IS - 12
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
1216 476 downloads 330 downloads
  
  
           

Abstract

This paper introduces a new method to improve visual search results and understand structured data. While many online resources teach basics of web development, few of them are designed to help novices learn the web development concepts and design patterns used by experts to implement complex visual features. Professional web pages embed these design patterns and could serve as rich learning materials, but their metadata are complex and difficult for novices to understand. This paper presents Metadata.py*, a Metadata inspection tool that helps novices use their visual intuition to make sense of professional web pages/sites. We introduce a new visual relevance testing technique to identify properties that have visual search results, which Metadata.py uses to hide visually irrelevant code and surface unintuitive relationships between properties. In user studies, Metadata.py helped novice developers replicate complex web features 75% faster than those using Chrome Developer Tool and allowed novices to recognize and explain unfamiliar concepts. These results show that visual inspection tools can support learning from complex professional web pages, even for novice developers. Metadata,py: Python Script using Beautiful Soup, a python library for pulling data out of HTML and XML files.

Key-Words / Index Term

Visual Search, SEO, Optimization

References

[1] M. L. Bernard. Examining the effects of hypertext shape on user performance. Usability News, 4(2), 2002.]]
[2] An Attribute-Assisted Reranking Model for Web Image Search, Isroset-Journal (IJSRCSE) Vol.4 , Issue.3 , pp.16-19, Jun-2016
[3] L. H. Armitage and P. G. B. Enser. Analysis of user need in image archives. Journal of Information Science, 23(4):287--299, 1997.]]
[4] P. Borland and P. Ingwersen. The development of a method for the evaluation of interactive information retrieval systems. Journal of Documentation, 53(3):225--250, 1997.]]
[5] R. C. Veltkamp and M. Tanase. Content-Based Image Retrieval Systems: A Survey. Technical Report UU-CS-2000-34, Dept. of Computing Science, Utrecht University, 2000.]].
[6] Greenberg, Jane, M. Pattuelli, B. Parsia, W. Robertson, Author-generated Dublin Core Metadata for Web Resources: A Baseline Study in an Organization, Journal of Digital Information, volume 2 issue 2 (November 2001)
[7] Heery, Rachel and Manjula Patel, Application Profiles: Mixing and Matching Metadata Schemas, Ariadne, Issue 25 (September 2000)