SIFT is a well-known feature extract algorithm for image matching. Due to the high dimensionality caused by the feature descriptor, it is too time consuming for real-time image retrieval application with kd-tree. To fast the image retrieval process, we deal with it in two aspects. Firstly, a novel method is proposed to reduce the dimensionality of the sift descriptor, which significantly drops the memory consumption caused by the millions of key points. Secondly, in kd-tree matching work, an improved decision algorithm is used for skipping some Euclidean distance computation. Experiment on our large book cover dataset demonstrated that the keypoint matching time can be reduced by over 25%. ? 2013 IEEE.EI
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for c...
Abstract. A new algorithm of feature matching was presented by this paper. In the new algorithm, Cur...
We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector e...
Retrieving book covers in web images or in specified database has a wide application prospect, from ...
Image matching is a fundamental aspect of many problems in computer vision, solving for 3D structur...
1 Work done as intern at Eastman Kodak Company The widespread utilization of digital visual media ha...
International audienceFig. 1. Illustration of CORE filtering with SIFT (keypoints+features) for p = ...
Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k ...
descriptors, and they have shown that the SIFTpack representation saves not only storage space, but ...
Comunicació presentada a la 22nd International Conference on MultiMedia Modeling (MMM16), celebrada ...
[[abstract]]Feature matching plays a key role in many image processing applications. To be robust a...
Feature matching has been frequently applied in computer vision and pattern recognition. In this pap...
This paper describes a new approach to document classification based on visual features alone. Text-...
This paper introduces a hybrid method for searching large image datasets for approximate nearest nei...
Abstract—Most of the content-based image retrieval systems require a distance computation for each c...
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for c...
Abstract. A new algorithm of feature matching was presented by this paper. In the new algorithm, Cur...
We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector e...
Retrieving book covers in web images or in specified database has a wide application prospect, from ...
Image matching is a fundamental aspect of many problems in computer vision, solving for 3D structur...
1 Work done as intern at Eastman Kodak Company The widespread utilization of digital visual media ha...
International audienceFig. 1. Illustration of CORE filtering with SIFT (keypoints+features) for p = ...
Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k ...
descriptors, and they have shown that the SIFTpack representation saves not only storage space, but ...
Comunicació presentada a la 22nd International Conference on MultiMedia Modeling (MMM16), celebrada ...
[[abstract]]Feature matching plays a key role in many image processing applications. To be robust a...
Feature matching has been frequently applied in computer vision and pattern recognition. In this pap...
This paper describes a new approach to document classification based on visual features alone. Text-...
This paper introduces a hybrid method for searching large image datasets for approximate nearest nei...
Abstract—Most of the content-based image retrieval systems require a distance computation for each c...
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for c...
Abstract. A new algorithm of feature matching was presented by this paper. In the new algorithm, Cur...
We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector e...