Botanical data retrieval system supporting discovery learning

We constructed a botanical data retrieval system applying our proposed search interface named 'Concentric Ring View' for multi-faceted metadata. This system allows users to search flexibly and intuitively by combining attributes with simple operation. The attributes used as search keys are visual and botanical features such as flower color, leaf shape, blooming season, and so on. Users can create their own dynamic knowledge hierarchies by selecting these attributes and adjusting attribute values. We considered that this system enables users not only to search for plant names but also to learn morphological features and the taxonomy of plants, and performed a usability testing. We confirmed that users could finally find the correct plant names even if each user selected different attributes for searching, and users could also find correct plants by grasping visual features even if the images shown focused on flower and leaf. In discovery learning, users could learn plant features and botanical properties by finding out the common properties from the relationships between attributes and attribute values, and attribute values and retrieved results.

[1]  Wei Liu,et al.  MQSearch: image search by multi-class query , 2008, CHI.

[2]  Allison Druin,et al.  The evolution of the international children's digital library searching and browsing interface , 2006, IDC '06.

[3]  Meng Wang,et al.  Visual query suggestion , 2009, ACM Multimedia.

[4]  Shin'ichi Satoh,et al.  Construction of image retrieval systems focused on user knowledge interaction , 2010, ACM Multimedia.

[5]  Gregory Scott,et al.  Idea navigation: structured browsing for unstructured text , 2008, CHI.

[6]  Hao Xu,et al.  Interactive image search by 2D semantic map , 2010, WWW '10.

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

[8]  Rong Yan,et al.  Multi-query interactive image and video retrieval -: theory and practice , 2008, CIVR '08.

[9]  Shih-Fu Chang,et al.  CuZero: embracing the frontier of interactive visual search for informed users , 2008, MIR '08.

[10]  Shelda Debowski,et al.  Wrong way: go back! An exploration of novice search behaviours while conducting an information search , 2001, Electron. Libr..

[11]  Shin'ichi Satoh,et al.  Examination and enhancement of a ring-structured graphical search interface based on usability testing , 2005, SIGIR '05.

[12]  Kristian J. Hammond,et al.  Knowledge-Based Navigation of Complex Information Spaces , 1996, AAAI/IAAI, Vol. 1.

[13]  Gary Marchionini,et al.  Evaluation and evolution of a browse and search interface: relation browser , 2005, DG.O.

[14]  B. C. Brookes The foundations of information science. Part I. Philosophical aspects , 1980 .

[15]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[16]  Marcel Worring,et al.  Optimization of interactive visual-similarity-based search , 2008, TOMCCAP.

[17]  Kevin Li,et al.  Faceted metadata for image search and browsing , 2003, CHI '03.

[18]  M. J. Enenhofer Spatial-Query-by-Sketch , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[19]  Shi-Min Hu,et al.  Sketch2Photo: internet image montage , 2009, ACM Trans. Graph..

[20]  Marti A. Hearst Clustering versus faceted categories for information exploration , 2006, Commun. ACM.