Relevance Feedback in Image Retrieval Systems: A Survey

Relevance Feedback in Image Retrieval Systems: A Survey

Relevance Feedback in Image Retrieval Systems: A Survey Part II Lin Luo, Tao Huang, Chengcui Zhang School of Computer Science Florida International University Introduction Multi-level Image Model For RF

Model Formalization RF based on this model Retrieval System Examples FourEyes(PhotoBook) PicToSeek Model Formalization

An image object O is represented as O = O ( D, F, R) D: raw image data, e.g. a JPEG image. F = { fi }: low-level visual features. R = { rij }: representations for a given fi. where rij =[ rij1, rij2 ,rijK ] K is the length of the vector. Model Formalization (cont.) Support weights

This object model supports multiple representations of weights based on the content of image objects. Weights exist at various levels. Wi, Wij, and Wijk, are associated with features fi, representations rij, and components rijk, respectively. RF Based on This Model The goal of RF based on this model is to find the appropriate weights to model the users information request.

RF method based on this model has been effectively developed in some systems, such as Mars97, MindReader and etc. Retrieval System Examples FourEyes(Photobook)

About FourEyes RF Related Concepts in FourEyes RF Method in FourEyes PicToSeek About PicToSeek Image and Query Model in PicToSeek RF Method in PicToSeek

FourEyes About FourEyes An interactive, power-assisted tool for segmenting and annotating image, embedded in the most recent version of Photobook. FourEyes offers a practical way to get interactive performance by using RF technique RF Related Concepts in FourEyes Grouping: A set of image regions that are associated in some way.

FourEyes includes Within-image grouping and Acrossimage grouping. RF Method in FourEyes FourEyes forms compound groupings for users. FourEyes adapt the grouping to the users needs with user feedback

FourEyes uses adaptive weighting mechanism. Each grouping has its own weight. User adds the grouping which maximizes the product of this number and the prior weight of the grouping to the compound grouping. Interface for PhotoBook http://vismod.www.media.mit.edu/~tpminka/photobook About PicToSeek An image search engine for cataloging and search images on the Web entirely on the basis of the pictorial content, proposed by Gevers et al in Univ. of Amsterdam, The Netherlands.

Image and Query Model in PicToSeek Each image can be presented by its image vectors as I ( f 0 , wI 0 ; f 1 , wI 1 ;...; f n , wIn ) Query is presented by its corresponding image vectors Q in the same form Q (Q0 , wQ 0 ; Q1 , wQ1 ;...; Qn , wQn )

Image and Query Model in PicToSeek (cont.) Formula used in PicToSeek to assign weights wi (0.5 0.5 ff i N ) log(

) n n max{ ff }i 1 Similarity function used in PicToSeek S (Q, I ) n k 1 min(wQk , wIk )

n k 1 wQk RF Methods in PicToSeek Main Idea Users feed back negative/positive images information. System learns which image features are more

important from users feedback and find the images according to the new features weighting. RF Method in PicToSeek RF process Formula Q ' Q rel Di Di | Di |

nonrel | Di | Use this formula to produce improved query specification. User need not to give a precise initial query formulation, the RF technique can move the query into the user-desired direction. Interface for PicToSeek http://zomax.wins.uva.nl:5345/ret_user/ Performance

Greatly reduce the users effort of composing a query. Capture the users information need more precisely. Conclusion All the approaches described above perform RF at the lowlevel feature vector level, but failed to take into account the actual semantics for the images themselves. There have been some efforts on incorporating semantics in RF for image retrieval, which will be covered in the next presentation.

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