An Artificial Imagination for Interactive Search

In this paper we take a look at the predominant form of human computer interaction as used in image retrieval, called interactive search, and discuss a new approach called artificial imagination. This approach addresses two of the grand challenges in this field as identified by the research community: reducing the amount of iterations before the user is satisfied and the small sample problem. Artificial imagination will deepen the level of interaction with the user by giving the computer the ability to think along by synthesizing ('imagining') example images that ideally match all or parts of the picture the user has in mind. We discuss two methods of how to synthesize new images, of which the evolutionary synthesis approach receives our main focus.

[1]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[3]  Nicu Sebe,et al.  Wavelet based texture classification , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Wei-Ying Ma,et al.  Learning a semantic space from user's relevance feedback for image retrieval , 2003, IEEE Trans. Circuits Syst. Video Technol..

[5]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[6]  Christian Böhm,et al.  Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases , 2001, CSUR.

[7]  Bart Thomee,et al.  Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video , 2004, MIR '04.

[8]  Klara Nahrstedt,et al.  Proceedings of the 24th ACM international conference on Multimedia , 2006, MM 2006.

[9]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[10]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[11]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[12]  C. W. Therrien,et al.  Decision, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics , 1989 .

[13]  Steve McLaughlin,et al.  Comparative study of textural analysis techniques to characterise tissue from intravascular ultrasound , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  Nicu Sebe,et al.  Facial expression recognition from video sequences , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[15]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[16]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[17]  Shih-Fu Chang,et al.  Semantic visual templates: linking visual features to semantics , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Bir Bhanu,et al.  Integrating relevance feedback techniques for image retrieval using reinforcement learning , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Thierry Pun,et al.  Strategies for positive and negative relevance feedback in image retrieval , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[20]  Guodong Guo,et al.  Boosting for content-based audio classification and retrieval: an evaluation , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[21]  Wei-Ying Ma,et al.  Learning and inferring a semantic space from user's relevance feedback for image retrieval , 2002, MULTIMEDIA '02.

[22]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[23]  Mark J. Huiskes,et al.  Aspect-Based Relevance Learning for Image Retrieval , 2005, CIVR.

[24]  Hwann-Tzong Chen,et al.  Semantic manifold learning for image retrieval , 2005, ACM Multimedia.