Efficient Image Classification on Vertically Decomposed Data

Organizing digital images into semantic categories is imperative for effective browsing and retrieval. In large image collections, an efficient algorithm is crucial to quickly categorize the new images. In this paper, we study a nearest neighbor based algorithm in image classification from a different perspective. The proposed algorithm vertically decomposes image features into separate bit vectors, one for each bit position of the values in the features, and approximates a number of candidates of nearest neighbors by examining the absolute difference of total variation between the images in the repositories and the unclassified image. Once the candidate set is obtained, the k-nearest neighbors are searched from that set. We use a combination of global color histogram in HSV (6x3x3) color space and Gabor texture for the image features. Our experiments on Corel dataset show that our algorithm is fast and scalable. The classification accuracy is very comparable to the accuracy of the cl...

• Type: Conference
• Year: 2006
• Where: ICDE
• Authors: Taufik Abidin, Aijuan Dong, Honglin Li, William Perrizo
• Keywords: Algorithm, Database, ICDE 2006, Image, Nearest Neighbors