Weighted Link Analysis for Logo and Trademark Image Retrieval on the Web Epimenidis Voutsakis * Euripides G.M. Petrakis * Evangelos Milios ** * Technical University of Crete ** Dalhousie University

6/17/2015http:// Image Retrieval on the Web  Text queries  Answers: images in Web pages Relevant but not always important From corporate web sites, organizations From individuals and small companies  Link analysis: assign higher ranking to answers from important web site Important doesn’t mean relevant !!

6/17/2015http:// Link Analysis  PageRank and HITS for text retrieval  PicASHOW for Web pages with images  WPicASHOW handles image and text content in queries and Web pages  Main idea: co-cited and co-contained images are likely to be related

6/17/2015http:// Image descriptions as  Text surrounding images in Web pages Image filename, Alternate text, Page title, Caption  Image features Intensity histogram, Energy spectrum, Moment invariants

6/17/2015http:// Example

6/17/2015http:// WPicASHOW  Queries are matched against text and image descriptions on links  Create the focused sub-graph F  Authorities: principal eigenvector of [(W+I)M T ](W+I)M W: page to page relationships in F M: page to image relationships in F  Rank answers by authority value

6/17/2015http:// Text Queries

6/17/2015http:// Queries by text and image

6/17/2015http:// Conclusions  Text retrieval: accurate Relevant but not always important answer  PicASHOW retrieves important but not always relevant answers  WPicASHOW: good compromise between relevance and importance Handles image content and queries by image example

6/17/2015http:// Web Implementation  Try WPicASHOW at   Over than 1.5 million pages with images  Selection of retrieval method  Link analysis method  And more..