No abstract
Fine-grained categorization is an essential field in classification, a subfield of object recognition that aims to differentiate subordinate classes. Fine-grained image classification concentrates on distinguishing between similar, hard-to-differentiate types or species, for example, flowers, birds, or specific animals such as dogs or cats, and identifying airplane makes or models. An important step towards fine-grained classification is the acquisition of datasets and baselines; hence, we propose a holistic system and two novel datasets, including reef fish and butterflies, for fine-grained classification. The butterflies and fish can be imaged at various locations in the image plane; thus, causing image variations due to translation, rotation, and deformation in multiple directions can induce variations, and depending on the image acquisition device’s position, scales can be different. We evaluate the traditional algorithms based on quantized rotation and scale-invariant local image features and the convolutional neural networks (CNN) using their pre-trained models to extract features. The comprehensive evaluation shows that the CNN features calculated using the pre-trained models outperform the rest of the image representations. The proposed system can prove instrumental for various purposes, such as education, conservation, and scientific research. The codes, models, and dataset are publicly available.
Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.
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