Abstract

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.

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