2014 IEEE JAVA DATA MINING PROJECT Web image re ranking using query-specific semantic signatures

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Web Image Re-Ranking Using Query-Specific Semantic 
Signatures 
ABSTRACT 
Image re-ranking, as an effective way to improve the results of web-based 
image search, has been adopted by current commercial search engines. Given a 
query keyword, a pool of images is first retrieved by the search engine based on 
textual information. By asking the user to select a query image from the pool, the 
remaining images are re-ranked based on their visual similarities with the query 
image. A major challenge is that the similarities of visual features do not well 
correlate with images’ semantic meanings which interpret users’ search intention. 
On the other hand, learning a universal visual semantic space to characterize highly 
diverse images from the web is difficult and ineffic ient. In this paper, we propose a 
novel image re-ranking framework, which automatically offline learns different 
visual semantic spaces for different query keywords through keyword expansions. 
The visual features of images are projected into their related visual semantic spaces 
to get semantic signatures. At the online stage, images are re-ranked by comparing
their semantic signatures obtained from the visual semantic space specified by the 
query keyword. The new approach significantly improves both the accuracy and 
efficiency of image re-ranking. The original visual features of thousands of 
dimensions can be projected to the semantic signatures as short as 25 dimensions. 
Experimental results show that 20% 35% relative improvement has been achieved 
on re-ranking precisions compared with the stateof-the art methods. 
SYSTEM ANALYSIS 
Existing System: 
This is the most common form of text search on the Web. Most search 
engines do their text query and retrieval using keywords. The keywords based 
searches they usually provide results from blogs or other discussion boards. The 
user cannot have a satisfaction with these results due to lack of trusts on blogs etc. 
low precision and high recall rate. In early search engine that offered 
disambiguation to search terms. User intention identification plays an important 
role in the intelligent semantic search engine. 
Proposed System: 
We propose the semantic web based search engine which is also called as 
Intelligent Semantic Web Search Engines. We use the power of xml meta-tags 
deployed on the web page to search the queried information. The xml page will be 
consisted of built-in and user defined tags. Here propose the intelligent semantic 
web based search engine. We use the power of xml meta-tags deployed on the web 
page to search the queried information. The xml page will be consisted of built-in 
and user defined tags. The metadata information of the pages is extracted from this
xml into rdf. our practical results showing that proposed approach taking very less 
time to answer the queries while providing more accurate information. 
MODULE DESCRIPTION 
1. Information retrieval. 
2. Search engine. 
1. Information retrieval. 
Information retrieval by searching information on the web is not a fresh idea 
but has different challenges when it is compared to general information retrieval. 
Different search engines return different search results due to the variation in 
indexing and search process. 
2. Search engine. 
Our search engine first searches the pages and then gets the result searching 
for the metadata to get the trusted results search engines require searching for 
pages that maintain such information at some place. Here propose the intelligent 
semantic web based search engine. we use the power of xml meta-tags deployed on 
the web page to search the queried information. the xml page will be cons isted of 
built-in and user defined tags our practical results showing that proposed approach 
taking very less time to answer the queries while providing more accurate 
information.
SYSTEM SPECIFICATION 
Hardware Requirements 
• System : Pentium IV 2.4 GHz. 
• Hard Disk : 80 GB. 
• Floppy Drive : 1.44 Mb. 
• Monitor : 15’ VGA Colour. 
• Mouse : Optical Mouse 
• RAM : 512 MB. 
Software Requirements: 
• Operating system : Windows XP. 
• Coding Language : ASP.Net with C# 
• Data Base : SQL Server 2005 
CONCLUSION 
We propose a novel image re-ranking framework, which learns query-specific 
semantic spaces to significantly improve the effectiveness and efficiency 
of online image reranking. The visual features of images are projected into their
related visual semantic spaces automatically learned through keyword expansions 
at the offline stage. The extracted semantic signatures can be 70 times shorter than 
the original visual feature on average, while achieve 20%35% relative 
improvement on re-ranking precisions over state-ofthe-art methods.

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2014 IEEE JAVA DATA MINING PROJECT Web image re ranking using query-specific semantic signatures

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Web Image Re-Ranking Using Query-Specific Semantic Signatures ABSTRACT Image re-ranking, as an effective way to improve the results of web-based image search, has been adopted by current commercial search engines. Given a query keyword, a pool of images is first retrieved by the search engine based on textual information. By asking the user to select a query image from the pool, the remaining images are re-ranked based on their visual similarities with the query image. A major challenge is that the similarities of visual features do not well correlate with images’ semantic meanings which interpret users’ search intention. On the other hand, learning a universal visual semantic space to characterize highly diverse images from the web is difficult and ineffic ient. In this paper, we propose a novel image re-ranking framework, which automatically offline learns different visual semantic spaces for different query keywords through keyword expansions. The visual features of images are projected into their related visual semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing
  • 2. their semantic signatures obtained from the visual semantic space specified by the query keyword. The new approach significantly improves both the accuracy and efficiency of image re-ranking. The original visual features of thousands of dimensions can be projected to the semantic signatures as short as 25 dimensions. Experimental results show that 20% 35% relative improvement has been achieved on re-ranking precisions compared with the stateof-the art methods. SYSTEM ANALYSIS Existing System: This is the most common form of text search on the Web. Most search engines do their text query and retrieval using keywords. The keywords based searches they usually provide results from blogs or other discussion boards. The user cannot have a satisfaction with these results due to lack of trusts on blogs etc. low precision and high recall rate. In early search engine that offered disambiguation to search terms. User intention identification plays an important role in the intelligent semantic search engine. Proposed System: We propose the semantic web based search engine which is also called as Intelligent Semantic Web Search Engines. We use the power of xml meta-tags deployed on the web page to search the queried information. The xml page will be consisted of built-in and user defined tags. Here propose the intelligent semantic web based search engine. We use the power of xml meta-tags deployed on the web page to search the queried information. The xml page will be consisted of built-in and user defined tags. The metadata information of the pages is extracted from this
  • 3. xml into rdf. our practical results showing that proposed approach taking very less time to answer the queries while providing more accurate information. MODULE DESCRIPTION 1. Information retrieval. 2. Search engine. 1. Information retrieval. Information retrieval by searching information on the web is not a fresh idea but has different challenges when it is compared to general information retrieval. Different search engines return different search results due to the variation in indexing and search process. 2. Search engine. Our search engine first searches the pages and then gets the result searching for the metadata to get the trusted results search engines require searching for pages that maintain such information at some place. Here propose the intelligent semantic web based search engine. we use the power of xml meta-tags deployed on the web page to search the queried information. the xml page will be cons isted of built-in and user defined tags our practical results showing that proposed approach taking very less time to answer the queries while providing more accurate information.
  • 4. SYSTEM SPECIFICATION Hardware Requirements • System : Pentium IV 2.4 GHz. • Hard Disk : 80 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15’ VGA Colour. • Mouse : Optical Mouse • RAM : 512 MB. Software Requirements: • Operating system : Windows XP. • Coding Language : ASP.Net with C# • Data Base : SQL Server 2005 CONCLUSION We propose a novel image re-ranking framework, which learns query-specific semantic spaces to significantly improve the effectiveness and efficiency of online image reranking. The visual features of images are projected into their
  • 5. related visual semantic spaces automatically learned through keyword expansions at the offline stage. The extracted semantic signatures can be 70 times shorter than the original visual feature on average, while achieve 20%35% relative improvement on re-ranking precisions over state-ofthe-art methods.