Background:
Image retrieval has a significant role in present and upcoming usage for different
image processing applications where images within a desired range of similarity are retrieved for a query
image. Representation of image feature, accuracy of feature selection, optimal storage size of feature vector
and efficient methods for obtaining features plays a vital role in Image retrieval, where features are represented
based on the content of an image such as color, texture or shape. In this work an optimal feature
vector based on control points of a Bezier curve is proposed which is computation and storage efficient.
Aim: To develop an effective and storage, computation efficient framework model for retrieval and classification
of plant leaves.
Objective:
The primary objective of this work is developing a new algorithm for control point extraction
based on the global monitoring of edge region. This observation will bring a minimization in false feature
extraction. Further, computing a sub clustering feature value in finer and details component to enhance the
classification performance. Finally, developing a new search mechanism using inter and intra mapping of
feature value in selecting optimal feature values in the estimation process.
Methods:
The work starts with the pre-processing stage that outputs the boundary coordinates of shape
present in the input image. Gray scale input image is first converted into binary image using binarization
then, the curvature coding is applied to extract the boundary of the leaf image. Gaussian Smoothening is
then applied to the extracted boundary to remove the noise and false feature reduction. Further interpolation
method is used to extract the control points of the boundary. From the extracted control points the
Bezier curve points are estimated and then Fast Fourier Transform (FFT) is applied on the curve points to
get the feature vector. Finally, the K-NN classifier is used to classify and retrieve the leaf images.
Results:
The performance of proposed approach is compared with the existing state-of-the-artmethods
(Contour and Curve based) using the evaluation parameters viz. accuracy, sensitivity, specificity, recall
rate, and processing time. Proposed method has high accuracy with acceptable specificity and sensitivity.
Other methods fall short in comparison to proposed method. In case of sensitivity and specificity Contour
method out performs proposed method. But in case accuracy and specificity proposed method outperforms
the state-of-the-art methods.
Conclusion:
This work proposed a linear coding of Bezier curve control point computation for image
retrieval. This approach minimizes the processing overhead and search delay by reducing feature vectors
using a threshold-based selection approach. The proposed approach has an advantage of distortion suppression
and dominant feature extraction simultaneously, minimizing the effort of additional filtration
process. The accuracy of retrieval for the developed approach is observed to be improved as compared to
the tangential Bezier curve method and conventional edge and contour-based coding. The approach signifies
an advantage in low resource overhead in computing shape feature.
An Novel Framework For Content Based Image Retrieval With Quality Assessment System using Optimal Deep Convolution Neural Network
