Content-Based Bird Retrieval using Shape context, Color moments and Bag of Features

TL;DR

This paper introduces a new bird image descriptor combining shape and color features, utilizing shape context, color moments, and bag of visual words to improve content-based bird retrieval accuracy.

Contribution

The paper presents a novel descriptor that integrates shape and color information for bird search, enhancing robustness and effectiveness over existing methods.

Findings

The proposed descriptor improves bird retrieval accuracy.

Combining shape and color features enhances robustness.

Experimental results validate the effectiveness of the descriptor.

Abstract

In this paper we propose a new descriptor for birds search. First, our work was carried on the choice of a descriptor. This choice is usually driven by the application requirements such as robustness to noise, stability with respect to bias, the invariance to geometrical transformations or tolerance to occlusions. In this context, we introduce a descriptor which combines the shape and color descriptors to have an effectiveness description of birds. The proposed descriptor is an adaptation of a descriptor based on the contours defined in article Belongie et al. [5] combined with color moments [19]. Specifically, points of interest are extracted from each image and information's in the region in the vicinity of these points are represented by descriptors of shape context concatenated with color moments. Thus, the approach bag of visual words is applied to the latter. The experimental results show the effectiveness of our descriptor for the bird search by content.