Ev ve Ofis taşıma sektöründe lider olmak.Teknolojiyi takip ederek bunu müşteri menuniyeti amacı için kullanmak.Sektörde marka olmak.
İstanbul evden eve nakliyat
Misyonumuz sayesinde edindiğimiz müşteri memnuniyeti ve güven ile müşterilerimizin bizi tavsiye etmelerini sağlamak.
On Mixture Model for
Image Databases
Presented by: Anlei Dong
Abstract:
Recently mixture model has been used to model image databases.
The retrieval experiences derived from multiple users' relevance
feedbacks have been exploited to improve model fitting in
a semi-supervised manner. However, the mixture model for image
databases remains a challenging task since the database may
contain clutter (the images not belonging to any class) and
outliers (the images being far away from the components corresponding
to the classes they belong to), and labeling information derived
from multiple users may be inconsistent. Thus, neither the
mixture model nor the labeling information is as ideal as
most of the researchers have previously assumed. In this paper,
we (a) address the challenges to handle the noise disturbances
for both mixture model and users' labeling information, (b)
propose to process retrieval experiences in an intelligent
manner using Bayesian analysis, (c) provide a novel probabilistic
multiple discriminant analysis for feature dimensionality
reduction, (d) present a robust mixture model fitting algorithm
to achieve visual concept learning, and (e) construct a concept-based
indexing structure for efficient search of the database. The
experimental results on two image databases show the correctness
of our retrieval experience analysis, the effectiveness of
the proposed concept learning approach, and the improvement
of retrieval performance based on the indexing structure.
|