2 research outputs found
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features
Due to an increase in the number of image achieves, Content-Based Image
Retrieval (CBIR) has gained attention for research community of computer
vision. The image visual contents are represented in a feature space in the
form of numerical values that is considered as a feature vector of image.
Images belonging to different classes may contain the common visuals and shapes
that can result in the closeness of computed feature space of two different
images belonging to separate classes. Due to this reason, feature extraction
and image representation is selected with appropriate features as it directly
affects the performance of image retrieval system. The commonly used visual
features are image spatial layout, color, texture and shape. Image feature
space is combined to achieve the discriminating ability that is not possible to
achieve when the features are used separately. Due to this reason, in this
paper, we aim to explore the low-level feature combination that are based on
color and shape features. We selected color moments and color histogram to
represent color while shape is represented by using invariant moments. We
selected this combination, as these features are reported intuitive, compact
and robust for image representation. We evaluated the performance of our
proposed research by using the Corel, Coil and Ground Truth (GT) image
datasets. We evaluated the proposed low-level feature fusion by calculating the
precision, recall and time required for feature extraction. The precision,
recall and feature extraction values obtained from the proposed low-level
feature fusion outperforms the existing research of CBIR
CBIR using features derived by Deep Learning
In a Content Based Image Retrieval (CBIR) System, the task is to retrieve
similar images from a large database given a query image. The usual procedure
is to extract some useful features from the query image, and retrieve images
which have similar set of features. For this purpose, a suitable similarity
measure is chosen, and images with high similarity scores are retrieved.
Naturally the choice of these features play a very important role in the
success of this system, and high level features are required to reduce the
semantic gap.
In this paper, we propose to use features derived from pre-trained network
models from a deep-learning convolution network trained for a large image
classification problem. This approach appears to produce vastly superior
results for a variety of databases, and it outperforms many contemporary CBIR
systems. We analyse the retrieval time of the method, and also propose a
pre-clustering of the database based on the above-mentioned features which
yields comparable results in a much shorter time in most of the cases.Comment: 18 pages, 31 figure