Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations

Joshi, A.; Agrawal, Ankit; Nair, Sushmita

doi:10.48550/arxiv.2012.03377

Cited by 2 publications

(2 citation statements)

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“…Chen et al [22] extracted the different features from various layers in the CNN structure, and the adaptive cross-layer correlation was used to classify the image styles. Joshi et al [23] extracted the features through an auto-encoder with random self-disturbance and trained the classifier in four machine-learning models for style recognition. Liu et al [24] calculated the color histogram and color semantic distance by SVM for traditional Chinese painting classification.…”

Section: Style-based Image Classificationmentioning

confidence: 99%

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Cui,

Zang,

Liu

et al. 2023

Buildings

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The rapid increase in building components on the building information model (BIM) object database has created new demand for BIM product recommendations to improve design efficiency. Current efforts mainly focus on the shape and contents of the products, instead of stylistic consistency, which is a crucial factor during the practical design process. To tackle such a problem, this paper proposes a novel framework to capture stylistic features based on long-range design dependencies with structural preservation, of which the snapshots of BIM products have been used to extract the stylistic features; core patches with strong style, generated by the pre-trained saliency model, are the root nodes; stylistic correlations are calculated as the hyperedges by tree-based operations; deep features and design features are proposed to represent the low-level and style distribution based on the study of design theory; and an ensemble learning strategy is introduced to solve the unbalanced classifier performance. An ablation study is conducted to validate the effectiveness of the proposed framework, in which comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

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Section: Style-based Image Classificationmentioning

confidence: 99%

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Cui,

Zang,

Liu

et al. 2023

Buildings

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show abstract

“…In most cases, these tasks are done in parallel. For the classification of artistic styles with high complexity due to high intra-class variation and low inter-class variation, self-supervised methods are also used [10]. Algorithms for the recognition of different painting techniques such as the clustered multiple kernel learning algorithm that extracts features from three aspects (colour, texture and spatial arrangement) for the recognition of oil paintings can be used for this task [15].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Painting Retrieval Based on Genre Similarity

Masclef,

Scuturici,

Bertin

et al. 2024

Lecture Notes in Computer Science

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As digitized paintings continue to grow in popularity and become more prevalent on online collection platforms, it becomes necessary to develop new image processing algorithms to effectively manage the paintings stored in databases. Image retrieval has historically been a challenging field within digital image processing, as it requires scanning large databases for images that are similar to a given query image. The notion of similarity itself, varies according to user's perception. The performance of image retrieval is heavily influenced by the feature representations and similarity measures used. Recently, Deep Learning has made significant strides, and deep features derived from this technology have become widely used due to their demonstrated ability to generalize well. In this paper, a fine-tune Convolutional Neural Network for the artistic genres recognition is employed to extract deep and high-level features from paintings. These features are then used to measure the similarity between a given query image and the images stored in the database, using an Approximate Nearest Neighbours algorithm to get a real time result. Our experimental results indicate this approach leads to a significant improvement in the performance of content-based image retrieval for the task of genre retrieval in paintings.

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Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations

Cited by 2 publications

References 0 publications

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

A Deep Learning Approach for Painting Retrieval Based on Genre Similarity

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