Visual cube and on-line analytical processing of images

On-Line Analytical Processing (OLAP) has shown great success in many industry applications, including sales, marketing, management, financial data analysis, etc. In this paper, we propose Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines (e.g., Google and Bing), product images (e.g. Amazon) and photos shared on social networks (e.g., Facebook and Flickr). It provides online responses to user requests with summarized statistics of image information and handles rich semantics related to image visual features. A clustering structure measure is proposed to help users freely navigate and explore images. Efficient algorithms are developed to construct Visual Cube. In addition, we introduce the new issue of Cell Overlapping in data cube and present efficient solutions for Visual Cube computation and OLAP operations. Extensive experiments are conducted and the results show good performance of our algorithms.

[1]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[2]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[3]  Angélica García Gutiérrez.  Applying OLAP Pre-Aggregation Techniques to Speed Up Query Response Times in Raster Image Databases , 2007, ICSOFT.

[4]  Jeffrey F. Naughton,et al.  An array-based algorithm for simultaneous multidimensional aggregates , 1997, SIGMOD '97.

[5]  Lei Zhang,et al.  IGroup: presenting web image search results in semantic clusters , 2007, CHI.

[6]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[7]  Anil K. Jain,et al.  Clustering ensembles: models of consensus and weak partitions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[9]  Wei Wang,et al.  Gradual Cube: Customize Profile on Mobile OLAP , 2006, Sixth International Conference on Data Mining (ICDM'06).

[10]  Joydeep Ghosh,et al.  A distributed learning framework for heterogeneous data sources , 2005, KDD '05.

[11]  Jian Pei,et al.  Ix-cubes: iceberg cubes for data warehousing and olap on xml data , 2007, CIKM '07.

[12]  Jiawei Han,et al.  MultiMediaMiner: a system prototype for multimedia data mining , 1998, SIGMOD '98.

[13]  Chun Kit Chui.  The design and implementation of an OLAP system for sequence data analysis , 2008, IDAR '08.

[14]  Arie Shoshani,et al.  On the performance of bitmap indices for high cardinality attributes , 2004, VLDB.

[15]  Sangkyum Kim,et al.  GAD: General Activity Detection for Fast Clustering on Large Data , 2009, SDM.

[16]  Angélica García Gutiérrez,et al.  Applying OLAP Pre-Aggregation Techniques to Speed Up Query Processing in Raster-Image Databases , 2007 .

[17]  Mingjie Xu.  Experiments on Remote Sensing image cube and its OLAP , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[18]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[19]  Bo Zhao,et al.  Text Cube: Computing IR Measures for Multidimensional Text Database Analysis , 2008, 2008 Eighth IEEE International Conference on Data Mining.