Conference proceeding
Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, v 5476, pp 867-874
01 Jan 2009
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 it 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.
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Details
- Title
- Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
- Creators
- Xin Chen - Drexel UniversityXiaohua Hu - Drexel UniversityXiajiong Shen - Henan University
- Contributors
- T Theeramunkong (Editor)B Kijsirikul (Editor)N Cercone (Editor)T B Ho (Editor)
- Publication Details
- ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, v 5476, pp 867-874
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 2
- Grant note
- IIS 0448023; CCF 0514679 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Radiation Oncology (and Nuclear Medicine)
- Web of Science ID
- WOS:000268632000087
- Scopus ID
- 2-s2.0-67650680229
- Other Identifier
- 991019170138304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence