Abstract: In this paper, we present a system for content-based
retrieval of large database of classified satellite images, based on
user's relevance feedback (RF).Through our proposed system, we
divide each satellite image scene into small subimages, which stored
in the database. The modified radial basis functions neural network
has important role in clustering the subimages of database according
to the Euclidean distance between the query feature vector and the
other subimages feature vectors. The advantage of using RF
technique in such queries is demonstrated by analyzing the database
retrieval results.
Abstract: In recent years, rapid advances in software and hardware in the field of information technology along with a digital imaging revolution in the medical domain facilitate the generation and storage of large collections of images by hospitals and clinics. To search these large image collections effectively and efficiently poses significant technical challenges, and it raises the necessity of constructing intelligent retrieval systems. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images[5]. Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes.
Abstract: In this paper, we present local image descriptor using
VQ-SIFT for more effective and efficient image retrieval. Instead of
SIFT's weighted orientation histograms, we apply vector quantization
(VQ) histogram as an alternate representation for SIFT features.
Experimental results show that SIFT features using VQ-based local
descriptors can achieve better image retrieval accuracy than the
conventional algorithm while the computational cost is significantly
reduced.
Abstract: The state-of-the-art Bag of Words model in Content-
Based Image Retrieval has been used for years but the relevance
feedback strategies for this model are not fully investigated. Inspired
from text retrieval, the Bag of Words model has the ability to use the
wealth of knowledge and practices available in text retrieval. We
study and experiment the relevance feedback model in text retrieval
for adapting it to image retrieval. The experiments show that the
techniques from text retrieval give good results for image retrieval
and that further improvements is possible.
Abstract: Retrieval image by shape similarity, given a template
shape is particularly challenging, owning to the difficulty to derive a
similarity measurement that closely conforms to the common
perception of similarity by humans. In this paper, a new method for the
representation and comparison of shapes is present which is based on
the shape matrix and snake model. It is scaling, rotation, translation
invariant. And it can retrieve the shape images with some missing or
occluded parts. In the method, the deformation spent by the template
to match the shape images and the matching degree is used to evaluate
the similarity between them.