Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Region-Based Image Querying   Retrieving images from large and varied repositories using visual contents
has been one of major research items, but a challenging task in the image
management community. In this paper we present an efficient approach for
region-based image classification and retrieval using a fast multi-level neural
network model. The advantages of this neural model in image classification and
retrieval domain will be highlighted. The proposed approach accomplishes its
goal in three main steps. First, with the help of a mean-shift based
segmentation algorithm, significant regions of the image are isolated.
Secondly, color and texture features of each region are extracted by using
color moments and 2D wavelets decomposition technique. Thirdly the multi-level
neural classifier is trained in order to classify each region in a given image
into one of five predefined categories, i.e., "Sky", "Building", "SandnRock",
"Grass" and "Water". Simulation results show that the proposed method is
promising in terms of classification and retrieval accuracy results. These
results compare favorably with the best published results obtained by other
state-of-the-art image retrieval techniques.

12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess