539 research outputs found
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
Representation Learning with Adversarial Latent Autoencoders
A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wisesimilarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon the explicit maximum likelihood training paradigm, as opposed to an implicit one. Likelihood maximization, coupled with poor decoder distribution leads to poor or blurry reconstructions at best. Generative Adversarial Networks (GANs) on the other hand, perform an implicit maximization of the likelihood by solving a minimax game, thus bypassing the issues derived from the explicit maximization. This provides GAN architectures with remarkable generative power, enabling the generation of high-resolution images of humans, which are indistinguishable from real photos to the naked eye. However, GAN architectures lack inference capabilities, which makes them unsuitable for training encoder-decoder maps, effectively limiting their application space.We introduce an autoencoder architecture that (a) is free from the consequences ofmaximizing the likelihood directly, (b) produces reconstructions competitive in quality with state-of-the-art GAN architectures, and (c) allows learning disentangled representations, which makes it useful in a variety of problems. We show that the proposed architecture and training paradigm significantly improves the state-of-the-art in novelty and anomaly detection methods, it enables novel kinds of image manipulations, and has significant potential for other applications
MinMax Radon Barcodes for Medical Image Retrieval
Content-based medical image retrieval can support diagnostic decisions by
clinical experts. Examining similar images may provide clues to the expert to
remove uncertainties in his/her final diagnosis. Beyond conventional feature
descriptors, binary features in different ways have been recently proposed to
encode the image content. A recent proposal is "Radon barcodes" that employ
binarized Radon projections to tag/annotate medical images with content-based
binary vectors, called barcodes. In this paper, MinMax Radon barcodes are
introduced which are superior to "local thresholding" scheme suggested in the
literature. Using IRMA dataset with 14,410 x-ray images from 193 different
classes, the advantage of using MinMax Radon barcodes over \emph{thresholded}
Radon barcodes are demonstrated. The retrieval error for direct search drops by
more than 15\%. As well, SURF, as a well-established non-binary approach, and
BRISK, as a recent binary method are examined to compare their results with
MinMax Radon barcodes when retrieving images from IRMA dataset. The results
demonstrate that MinMax Radon barcodes are faster and more accurate when
applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on
Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US
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