Retrieval of User Specific Images Using Semantic Signatures

Image search engines rely on the surrounding textual
keywords for the retrieval of images. It is a tedious work for the
search engines like Google and Bing to interpret the user’s search
intention and to provide the desired results. The recent researches
also state that the Google image search engines do not work well on
all the images. Consequently, this leads to the emergence of efficient
image retrieval technique, which interprets the user’s search intention
and shows the desired results. In order to accomplish this task, an
efficient image re-ranking framework is required. Sequentially, to
provide best image retrieval, the new image re-ranking framework is
experimented in this paper. The implemented new image re-ranking
framework provides best image retrieval from the image dataset by
making use of re-ranking of retrieved images that is based on the
user’s desired images. This is experimented in two sections. One is
offline section and other is online section. In offline section, the reranking
framework studies differently (reference classes or Semantic
Spaces) for diverse user query keywords. The semantic signatures get
generated by combining the textual and visual features of the images.
In the online section, images are re-ranked by comparing the
semantic signatures that are obtained from the reference classes with
the user specified image query keywords. This re-ranking
methodology will increases the retrieval image efficiency and the
result will be effective to the user.

• K. Venkateswari
• U. K. Balaji Saravanan
• K. Thangaraj
• K. V. Deepana

• CBIR
• Image Re-ranking
• Image Retrieval
• Semantic Signature
• Semantic Space.

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