There is a clear shift from traditional methods to sophisticated techniques this maximizes the utilization of the widely available digital media. Recent development in technology influenced our daily life and the way people communicate and store data. Experiment results show that the proposed method can improve the image retrieval precision effectively. Finally, the similarity is measured based on both the point feature and the shape feature. Hu invariant moment of the edge is used to represent the object's shape information. Annular histogram combined with standard deviation ellipse method is used to describe the spatial distribution of feature points.

Then, image features are described based on both the edge and points.

Firstly, edge of image is detected and feature points are calculated based on the edge curvature. In this paper, an image retrieval method based on Hu invariant moments and improved annular histogram is proposed. These disadvantages affect the retrieval accuracy to a certain extent. Another one is the methods based on feature points cannot describe the shape information of object. One is that the annular histogram can't describe the spatial distribution of feature points accurately, thus different images may have similar annular histogram. However, these methods have two main disadvantages. Image retrieval methods based on annular histogramof feature points are calculation efficient, invariant to image rotation and translation transform. Experimental results have shown that the proposed technique is effective in organic shapes classification to selected geometric shapes. In the classification step all the shifted and flipped centroid distance function variations of the testing sample are voting for the class using the decision tree. The training of classifier was made by acquiring all possible orientations of centroid distance function for each image in training set and then feeding them to decision tree. Using labeled samples the decision tree classifier was trained. Clusters labeled by an expert to same categories were merged. The k-medoids and k-means clustering algorithms were compared by generating clusters of similar shapes for labeling to one of geometric shapes: circle, ellipse, oval, triangle, rectangle, rhombus, trapezium, and trapezoid. The centroid distance function was selected for shape representation as it preserves the order of landmark points. The amber data used in experiments are collected by amber art craft industry experts and the presented investigations were care out in order to develop a classifier for online amber sorting application. It does not store any personal data.This paper proposes and describes a novel technique for organic shapes classification by similarity to basic geometric shapes. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category 'Performance'.

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Organic shapes