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[PDF] Top 20 Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach

Has 10000 "Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach" found on our website. Below are the top 20 most common "Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach".

Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach

Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach

... descriptors for image patches and Thoth team, Inria Grenoble Rhone-Alpes, Laboratoire Jean Kuntzmann, CNRS, ...of patch and image re- ...supervision for training such networks, ranging ... Voir le document complet

22

Attention for Image Registration (AiR): an unsupervised Transformer approach

Attention for Image Registration (AiR): an unsupervised Transformer approach

... in image/videos analysis implies that the Transformer have the potential power to show off in structure data ...need convolutional kernels for features representation but learns the data inherent ... Voir le document complet

10

Split and Match: Example-based Adaptive Patch Sampling for Unsupervised Style Transfer

Split and Match: Example-based Adaptive Patch Sampling for Unsupervised Style Transfer

... from an image based Markov Random Field (MRF), which we call here respectively as the greedy and the iterative ...from an example texture. A similar approach was extended to patch-based ... Voir le document complet

10

False Discovery Rate Approach to Unsupervised Image Change Detection

False Discovery Rate Approach to Unsupervised Image Change Detection

... the patch-based samples. The approach involves only a few parameters and is highly ...detection approach constitutes a flexible and statistically sound inference procedure that, nevertheless, has ... Voir le document complet

27

Learning compact representations for large scale image search

Learning compact representations for large scale image search

... systems for large scale image collections, as discussed in Chapter ...performance for very large-scale high-dimensional nearest- neighbor search (relative to the Euclidean distance in input feature ... Voir le document complet

111

Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification

Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification

... the patch-based approach discussed above, which motivate the design of an improved network ...last convolutional layer (before the last fully connected one) is 9 × ...input image, due ... Voir le document complet

14

Local Convolutional Features with Unsupervised Training for Image Retrieval

Local Convolutional Features with Unsupervised Training for Image Retrieval

... shallow patch descriptors, deep learning for image retrieval and deep learning for patch ...Traditional patch descriptors. Among the variety of stan- dard patch ... Voir le document complet

10

High-Dimensional Mixture Models For Unsupervised Image Denoising (HDMI)

High-Dimensional Mixture Models For Unsupervised Image Denoising (HDMI)

... [6] for a recent review of model-based clustering techniques for high-dimensional ...clustering for image denoising and contributions of the ...the image denoising ...external ... Voir le document complet

30

Invariance and Stability of Deep Convolutional Representations

Invariance and Stability of Deep Convolutional Representations

... motivation for introducing a kernel framework is to study separately data representation and predictive ...signal representations that are near-invariant to the action of any group of ...on image ... Voir le document complet

22

Fully Convolutional Neural Networks For Remote Sensing Image Classification

Fully Convolutional Neural Networks For Remote Sensing Image Classification

... Color image (b) Fuzzy map (c) Binary map ...Pleiades image, with an ...FCNs for the classification of a Pleiades image covering the area of Forez, France, at a ...the image, ... Voir le document complet

5

Multi-criteria Search Algorithm: An Efficient Approximate K-NN Algorithm for Image Retrieval

Multi-criteria Search Algorithm: An Efficient Approximate K-NN Algorithm for Image Retrieval

... search for each descriptor of the query, which is problematic when a large number of descriptors per image is ...of an image to a single vector (called signature) have been proposed [9, ...The ... Voir le document complet

6

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI

... methods, for both Dice and PSNR ...gap for Dice and ...coupling patch-based and iterative optimization is indeed of interest, as the results are at the level of the state of the ...algorithmic ... Voir le document complet

26

An axiomatic approach to image interpolation

An axiomatic approach to image interpolation

... literature for 'perceptually motivated' coding applications [5, 17, ...underlying image model is based on the concept of 'raw primal sketch' ...The image is assumed to be made mainly of areas of ... Voir le document complet

30

General pairwise Markov chains for unsupervised image segmentation

General pairwise Markov chains for unsupervised image segmentation

... with an exponential correlation function ...DNNs for the Deep model are set to one hidden layer with 10 ...give, for comparison purposes, the results of the K- means ... Voir le document complet

6

Efficient Indexing for Strongly Similar Image Retrieval

Efficient Indexing for Strongly Similar Image Retrieval

... 4.3 Image Retrieval Results Experimental results are summarized in the following figures, where exhaustive search is labeled as M SB = 0 in the ...performance for the first five databases described ... Voir le document complet

10

Image decomposition and separation using sparse representations: an overview

Image decomposition and separation using sparse representations: an overview

... that, for each k, the representation of x k in Φ k is sparse and not, or at least not as sparse, in other Φ l, l 6= ...observation for the success of the separation ...novel representations, ... Voir le document complet

18

Hash functions for near duplicate image retrieval

Hash functions for near duplicate image retrieval

... keys for each local ...that, for near dupli- cate retrieval, it performs better than kmeans-based vocab- ulary if the kmeans is not applied on the searched ...solution for applications where ... Voir le document complet

6

Early burst detection for memory-efficient image retrieval

Early burst detection for memory-efficient image retrieval

... search for repeating groups of features is too constrained and may fail to identify bursts, especially in natural ...orientation. For instance, similar patches in ... Voir le document complet

10

Early burst detection for memory-efficient image retrieval

Early burst detection for memory-efficient image retrieval

... for instance, we can keep only 50% features for a 4% drop in ...superior for low ...at an early stage, we make the in- verted file more ...at an aggregation% which gives at the same ... Voir le document complet

12

Explicit Small Image Theorems for Residual Modular Representations

Explicit Small Image Theorems for Residual Modular Representations

... Let k > 2, N > 1 be two integers, and ε be a Dirichlet character modulo N of conductor c. Let f ∈ S new k (N, ε) be a newform. We study in this section the case where ρ f,λ has projective dihedral image. The ... Voir le document complet

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