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Content Provider | IEEE Xplore Digital Library |
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Author | Kato, H. Harada, T. |
Copyright Year | 2014 |
Description | Author affiliation: Univ. of Tokyo, Tokyo, Japan (Kato, H.; Harada, T.) |
Abstract | The objective of this study is to reconstruct images from Bag-of-Visual-Words (BoVW), which is the de facto standard feature for image retrieval and recognition. BoVW is defined here as a histogram of quantized descriptors extracted densely on a regular grid at a single scale. Despite its wide use, no report describes reconstruction of the original image of a BoVW. This task is challenging for two reasons: 1) BoVW includes quantization errors when local descriptors are assigned to visual words. 2) BoVW lacks spatial information of local descriptors when we count the occurrence of visual words. To tackle this difficult task, we use a large-scale image database to estimate the spatial arrangement of local descriptors. Then this task creates a jigsaw puzzle problem with adjacency and global location costs of visual words. Solving this optimization problem is also challenging because it is known as an NP-Hard problem. We propose a heuristic but efficient method to optimize it. To underscore the effectiveness of our method, we apply it to BoVWs extracted from about 100 different categories and demonstrate that it can reconstruct the original images, although the image features lack spatial information and include quantization errors. |
Starting Page | 955 |
Ending Page | 962 |
File Size | 824038 |
Page Count | 8 |
File Format | |
ISBN | 9781479951185 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2014.127 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Visualization Image reconstruction Optimization Dictionaries Geometry Image retrieval image reconstruction computer vision recognition retrieval |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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