Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval

It is a challenging and important task to retrieve images from a large and highly varied image data set based on their visual contents. Problems like how to fill the semantic gap between image features and the user have attracted a lot of attention from the research community. Recently, the 'bag of visual words' approach exhibits very good performance in content-based image retrieval (CBIR). However, since the 'bag of visual words' approach represents an image as an unordered collection of local descriptors which only use the intensity information, the resulting model provides little insight about the spatial constitution and color information of the image. In this paper, we develop a novel image representation method which uses Gaussian mixture model (GMM) to provide spatial weighting for visual words and apply this method to facilitate content based image retrieval. Our approach is a simple and more efficient compared with the order-less 'bag of visual words&...

• Type: Conference
• Year: 2009
• Where: PAKDD
• Authors: Xin Chen, Xiaohua Hu, Xiajiong Shen
• Keywords: Data Mining, Image Retrieval, Images, PAKDD 2009, Visual Words