Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy

TL;DR

This paper presents a novel image retrieval method using COSFIRE descriptors for radio astronomy, achieving high accuracy and efficiency in identifying similar astronomical sources, especially for unknown or anomalous objects.

Contribution

The work introduces a COSFIRE-based approach combined with hashing for scalable, efficient, and accurate image retrieval in radio astronomy, outperforming DenseNet-based methods.

Findings

Achieved 91% mean average precision in retrieval tasks.

COSFIRE filters are significantly more computationally efficient, with 14 times fewer operations.

Effective in handling large datasets and identifying similar astronomical sources.

Abstract

The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine the sources that are most similar to a given query source. Image-based retrieval is essential, as it allows an astronomer with a source under study to ask a computer to sift through the large archived database of sources to find the most similar ones. This is of particular interest if the source under study does not fall into a "known" category (anomalous). Our work uses the trainable COSFIRE (Combination of Shifted Filter Responses) approach for image retrieval. COSFIRE filters are automatically configured to extract the hyperlocal geometric arrangements that uniquely describe the morphological characteristics of patterns of interest in a given image; in this case astronomical sources. This is achieved by automatically examining the shape properties of a given prototype source in an image, which ultimately determines the selectivity of a COSFIRE filter. We further utilize hashing techniques, which are efficient in terms of required computation and storage, enabling scalability in handling large data sets in the image retrieval process. We evaluated the effectiveness of our approach by conducting experiments on a benchmark data set of radio galaxies, containing 1,180 training images and 404 test images. Notably, our approach achieved a mean average precision of 91% for image retrieval, surpassing the performance of the competing DenseNet-based method. Moreover, the COSFIRE filters are significantly more computationally efficient, requiring $\sim\!14\times$ fewer operations than the DenseNet-based method.