Paper Info
Title
Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
Abstract
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' approach. In our method, firstly, we extract visual tokens from the image data set and cluster them into a lexicon of visual words. Then, we represent the spatial constitution of an image as a mixture of n Gaussians in the feature space and decompose the image into n regions. The spatial weighting scheme is achieved by weighting visual words according to the probability of each visual word belonging to each of the n regions in the image. The cosine similarity between spatial weighted visual word vectors is used as distance measurement between regions, while the image-level distance is obtained by averaging the pair-wise distances between regions. We compare the performance of our method with the traditional 'bag of visual words' and 'blobworld' approaches under the same image retrieval scenario. Experimental results demonstrate that the our method is able to tell images apart in the semantic level and improve the performance of CBIR.
Year
DOI
Venue
2009
10.1007/978-3-642-01307-2_90
PAKDD
Keywords
Field
DocType
novel image representation method,spatial weighting,varied image data,image data,spatial constitution,image retrieval scenario,image feature,visual word,spatial weighted visual word,image retrieval,content-based image retrieval,content-based image,gaussian mixture model,bag of visual words,feature space,image features,visual words,semantic gap
Computer vision,Automatic image annotation,Pattern recognition,Feature detection (computer vision),Bag-of-words model in computer vision,Computer science,Human visual system model,Image texture,Image retrieval,Artificial intelligence,Content-based image retrieval,Visual Word
Conference
Volume
ISSN
Citations 
5476
0302-9743
14
PageRank 
References 
Authors
0.66
13
3
Name
Order
Citations
PageRank
Xin Chen1423.41
Xiaohua Hu22819314.15
Xiajiong Shen314312.10