310,606 research outputs found
On Critical Relative Distance of DNA Codes for Additive Stem Similarity
We consider DNA codes based on the nearest-neighbor (stem) similarity model
which adequately reflects the "hybridization potential" of two DNA sequences.
Our aim is to present a survey of bounds on the rate of DNA codes with respect
to a thermodynamically motivated similarity measure called an additive stem
similarity. These results yield a method to analyze and compare known samples
of the nearest neighbor "thermodynamic weights" associated to stacked pairs
that occurred in DNA secondary structures.Comment: 5 or 6 pages (compiler-dependable), 0 figures, submitted to 2010 IEEE
International Symposium on Information Theory (ISIT 2010), uses IEEEtran.cl
フラクタル符号化特徴量を用いた類似画像検索およびオブジェクト検出手法の検討
Fractal image coding is a block-based scheme that exploits the self-similarity hiding with an image. Fractal codes are quantitative measurements of the self-similarity of the image, and collage error distribution of block characterizes the degree of self-similarity in it. Furthermore, fractal codes can be used to obtain a practical image indexing system because of its compactness and stability. The most important reason using fractal codes is able to deal with the images in compressed form. Thus fractal indexing is suitable for use with large database. In this study, we propose a new image retrieval system and object detection method based on fractal coding features that are collage error distribution and block partition structure in fractal codes. Experimental results show that the proposed method achieves a high precision tracking which is faster than MPEG method
Bilinear Random Projections for Locality-Sensitive Binary Codes
Locality-sensitive hashing (LSH) is a popular data-independent indexing
method for approximate similarity search, where random projections followed by
quantization hash the points from the database so as to ensure that the
probability of collision is much higher for objects that are close to each
other than for those that are far apart. Most of high-dimensional visual
descriptors for images exhibit a natural matrix structure. When visual
descriptors are represented by high-dimensional feature vectors and long binary
codes are assigned, a random projection matrix requires expensive complexities
in both space and time. In this paper we analyze a bilinear random projection
method where feature matrices are transformed to binary codes by two smaller
random projection matrices. We base our theoretical analysis on extending
Raginsky and Lazebnik's result where random Fourier features are composed with
random binary quantizers to form locality sensitive binary codes. To this end,
we answer the following two questions: (1) whether a bilinear random projection
also yields similarity-preserving binary codes; (2) whether a bilinear random
projection yields performance gain or loss, compared to a large linear
projection. Regarding the first question, we present upper and lower bounds on
the expected Hamming distance between binary codes produced by bilinear random
projections. In regards to the second question, we analyze the upper and lower
bounds on covariance between two bits of binary codes, showing that the
correlation between two bits is small. Numerical experiments on MNIST and
Flickr45K datasets confirm the validity of our method.Comment: 11 pages, 23 figures, CVPR-201
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