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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.
Multiple Local Kernel Integrated Feature Selection for Image Classification
Feature redundancy and loss of local feature are
central problems for image classification. Feature
selection decreases the feature redundancy by
choosing a subset of features and eliminating those
with low prediction. The local feature representation is
able to highlight objects in an image, thus, overcoming
the drawbacks of global features. We present
a new method, called the local kernel for feature
selection, which integrates a local kernel of the
segmentation regions into feature selection to provide
improved image classification, by means of the regionbased
image distance integrated into the kernel of the
Bayesian classifier. The proposed method is tested on
two standard image databases and the classification
results are higher than the current feature selection
and classification methods.
Image Retrieval with Feature Selection and Relevance Feedback
We propose a new content based image retrieval
(CBIR) system combined with relevance feedback and the
online feature selection procedures. A measure of
inconsistency from relevance feedback is explicitly used as
a new semantic criterion to guide the feature selection. By
integrating the user feedback information, the feature
selection is able to bridge the gap between low-level visual
features and high-level semantic information, leading to the
improved image retrieval accuracy. Experimental results
show that the proposed method obtains higher retrieval
accuracy than a commonly used approach.
Genetic Algorithm Based Feature Selection for Target Detection in SAR Images
A genetic algorithm (GA) approach is presented to select a set of features to discriminate the targets from
the natural clutter false alarms in SAR images. A new fitness function based on minimum
description length principle (MDLP) is proposed to drive GA and it is compared with three other fitness
functions. Experimental results show that the new fitness function outperforms the other three fitness
functions and the GA driven by it selected a good subset of features to discriminate the targets from
clutters effectively.
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