GPCA: an efficient dimension reduction scheme for image compression and retrieval

Recent years have witnessed a dramatic increase in the quantity of image data collected, due to advances in fields such as medical imaging, reconnaissance, surveillance, astronomy, multimedia etc. With this increase has come the need to be able to store, transmit, and query large volumes of image data efficiently. A common operation on image databases is the retrieval of all images that are similar to a query image. For this, the images in the database are often represented as vectors in a high-dimensional space and a query is answered by retrieving all image vectors that are proximal to the query image in this space, under a suitable similarity metric. To overcome problems associated with high dimensionality, such as high storage and retrieval times, a dimension reduction step is usually applied to the vectors to concentrate relevant information in a small number of dimensions. Principal Component Analysis (PCA) is a well-known dimension reduction scheme. However, since it works with...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Jieping Ye, Ravi Janardan, Qi Li
• Keywords: Data Mining, Dimension Reduction Scheme, KDD 2004, Principal Component Analysis, Query Image