  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.
