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IntentSearch: Capturing User Intention forOne-Click Internet Image Search
Presented by
Nayana.K.Raj0941943PAACET
26-07-2012
INTRODUCTION
•Search engine which helps to interpret users’ search intention by using ONE-CLICK query image
•Uses 4 steps for image searching
•Text based information of query word and visual content of query image to expand the image pool
EXPLANATION User search intention only by query
keywords is difficult because text based image search suffers from..
•Ambiguity of query keywords
•User doesn't have enough knowledge
•Hard for users to describe the visual content of target images
•Easier search by using both textual and visual content of query
•Web-scale image search engines mostly rely on surrounding text features.
•Users’ search intention by only by query keywords
PROPOSED SYSTEM• Image search on the basis of both
textual and visual content of images
•Image pool is re-ranked based on textual and visual features
EXISTING SYSTEM
Fig. 1: Top-ranked images returned from ‘Bing’ using
“apple” as query
Key contribution is to capture the users’ search intention from this one-click query image in four steps.
•Adaptive similarity
•Keyword expansion
•Visual query expansion
•Image pool expansion
SEARCH TECHNIQUES
The user first submits query keywords q. A pool of image is retrieved by text-based search User is asked to select the query image from image pool The query image is classified as one of the predefined adaptive weight categories Images in the pool are re-ranked based on their visual similarities to the query imageSimilarities are computed using the weight schema
Visual feature design
Existing features : Gist , SIFT, Daubechies Wavelet , Histogram of Gradient (HoG)
New features : Attention guided Color Signature, Color spatialet (CSpa) , Multilayer Rotation Invarient ( EOH), Facial Feauter
Adaptive Weight Schema
•Weight schema is used for similarity calculations
•Lets take image i and j… Adaptive similarity between i & j
Sq(i , j) = ∑fm=1
αmq sm(i,j)
where sm(i,j) similarity between I and j on feature mf is the visual featureαm
q is the express the importance of feature m for measuring similarity
existence of faces, the number of faces in the image
percentage of the image frame taken up by the face region
coordinate of face center relative to the centre of image
Directionality
Color Spatial Homogeneousness (variance of values in different blocks of Color Spatialet)
Total energy of edge map obtained from Canny Operator
Edge Spatial Distribution
Features for query categorization
•Image is divided into clusters•Each word wi has ti clusters
C(wi)= { ci,1 ,.............,ci,ti }
•Visual distance between the query image and a cluster c is calculated as the mean of the distances between the query image and the images in c.
•The cluster Ci,j with the minimal distance is chosen as visual query expansion and its corresponding word wi .
q = wi + q’
Image Clustering
• duplicate images
• User friendly
• Easy search for a particular image(on the internet)
• Can find the image is real or not
DISADVANTAGES
ADVANTAGES
FUTURE ENHANCEMENT
•query log data, which provides valuable co-occurrence information of keywords , for keyword expansion
•Can be improved by including duplicate detection in the future work
CONCLUSION
•Internet image search approach which only requires one-click user feedback
•Intention specific weight schema
•Without additional human feedback
•Possible for industrial scale image search by both text and visual content
REFERENCES
•J. Cui, F. Wen, and X. Tang, “Real Time Google and LiveImage Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.
• J. Cui, F. Wen, and X. Tang, “IntentSearch: Interactive On-Line Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia,2008.
• “Bing Image Search,” http://www.bing.com/images
•http://www.google.com/imagesearch