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Variational Exemplar-Based Image Colorization
Abstract:-
Interest point detection is an important research area in the field of image
processing and computer vision. In particular, image retrieval and object
categorization heavily rely on interest. Point detection from which local image
descriptors are computed for image matching. The use of color may therefore provide
selective search reducing the total number of interest points used for image matching.
This paper proposes color interest points for sparse image representation. A large-scale
experiment, it is shown that the proposed color interest point detector has higher
repeatability than a luminance-based one. Furthermore, in the context of image
retrieval, a reduced and predictable number of color features show an increase in
performance compared to state-of-the-art interest points. The contributions of this
paper are twofold. First, we introduce a variational formulation.Modeling the color
selection problem under spatial constraints and propose a minimization scheme, which
computes a local Minima of the defined non convex energy. Second, we combine
different patch-based features and distances in order to construct a consistent set of
possible color candidates. A first category of colorization algorithms requires

2. a huge amount of user intervention to manually add initial colors to the grayscale
image. The colorization process is then performed by propagating the input color data
to the whole image. First, several color candidates are computed using different
features and associated metrics. The final color is then obtained by solving a
variational model which allows the automatic selection of the best candidates
while adding some regularization in the solution.
Existing System:-
 The candidate selection and color regularization problems are simultaneously
solved through a variational energy minimization.
 Natural images contain different types of complex structures, redundancies and
textures.
 The problem of selecting the best color to transfer among the set of possible
colors is solved, for each pixel independently, using a dimensional reduction.
 We focus on the image colorization problem which involves consistent and
spatially coherent image results.
Proposed System:
 We explore methods for learning local image descriptors from training data We
describe a set of building blocks for constructing descriptors which can be
combined together.
 We consider both linear and nonlinear transforms with dimensionality
reduction.

3.  These techniques have state-of-the-art performance in all of our test scenarios;
have been used to design local feature descriptors for a robust structure.
 The color interest point detectors are presented and a scale selection method is
proposed.
Hardware Requirements:-
 SYSTEM : Pentium IV 2.4 GHz
 HARD DISK : 40 GB
 RAM : 256 MB
Software Requirements:-
 Operating system : Windows XP Professional
 IDE : Microsoft Visual Studio .Net 2005
 Coding Language : C# .NET