17 research outputs found
Efficient image copy detection using multi-scale fingerprints
Inspired by multi-resolution histogram, we propose
a multi-scale SIFT descriptor to improve the discriminability.
A series of SIFT descriptions with different scale are ļ¬rst
acquired by varying the actual size of each spatial bin. Then
principle component analysis (PCA) is employed to reduce them
to low dimensional vectors, which are further combined into one
128-dimension multi-scale SIFT description. Next, an entropy
maximization based binarization is employed to encode the
descriptions into binary codes called ļ¬ngerprints for indexing
the local features. Furthermore, an efļ¬cient search architecture
consisting of lookup tables and inverted image ID list is designed
to improve the query speed. Since the ļ¬ngerprint building is
of low-complexity, this method is very efļ¬cient and scalable to
very large databases. In addition, the multi-scale ļ¬ngerprints
are very discriminative such that the copies can be effectively
distinguished from similar objects, which leads to an improved
performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art
methods.Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods
Saliency detection using suitable variant of local and global consistency
In existing local and global consistency (LGC) framework, the cost functions related to classifying functions adopt the sum of each row of weight matrix as an important factor. Some of these classifying functions are successfully applied to saliency detection. From the point of saliency detection, this factor is inversely proportional to the colour contrast between image regions and their surroundings. However, an image region that holds a big colour contrast against it surroundings does not denote it must be a salient region. Therefore a suitable variant of LGC is introduced by removing this factor in cost function, and a suitable classifying function (SCF) is decided. Then a saliency detection method that utilises the SCF, contentābased initial label assignment scheme, and appearanceābased label assignment scheme is presented. Via updating the contentābased initial labels and appearanceābased labels by the SCF, a coarse saliency map and several intermediate saliency maps are obtained. Furthermore, to enhance the detection accuracy, a novel optimisation function is presented to fuse the intermediate saliency maps that have a high detection performance for final saliency generation. Numerous experimental results demonstrate that the proposed method achieves competitive performance against some recent stateāofātheāart algorithms for saliency detection