Affective image analysis aims to understand the sentiment of different images. The challenge is to develop a discriminative representation that bridges the affective gap between low-level features and high-level emotions. Most existing studies bridge the gap by designing deep models carefully to learn global representations in one shot directly or identify image emotion by extracting features at different levels in the model. They ignore that both local regions of an image and relationships between them impact emotional representation learning. This paper develops an affective image analysis method based on the aesthetic fusion hybrid attention network (AFHA). A modular hybrid attention block is designed to extract image emotion features and model long-range dependencies of images. By stacking hybrid attention blocks in ResNet-style, we obtain an affective representation backbone. Furthermore, considering that image emotion is inseparable from aesthetics, we employ a modified ResNet to extract image aesthetics. Finally, through a fusion strategy, the image's emotion is considered with the aesthetics conveyed. Experiments demonstrate the close relationship between emotion and aesthetics, and our plan has an excellent competitive effect compared with existing methods on the image sentiment analysis dataset.
Since the mineral pigments on the surface of Chinese paintings are small in particle size and the pigment layer is too thin to cover the paper completely, their spectral mixing often shows nonlinearity. In this paper, aimed to accurately estimate the different types of pigments abundance on the surface of Chinese paintings, we proposed an improved single-constant Kubelka-Munk (KM) method for pigments unmixing algorithm. First, we selected Malachite, Azurite and Cinnabar pigments and mixed them in pairs in different abundances. They were added glue and then painted on the rice paper to produce the samples. The spectra of pure pigments and their mixed samples were acquired by the instrument of ASD FieldSpec 4. Second, we calculated the absorption and scattering ratio of the samples, pure pigments and rice paper. The rice paper and pure pigments were regarded as endmembers together for the pigment layer is too thin to cover the paper completely. The abundances were figured out base on the Fully Constrained Least Squares (FCLS) method endmember. Third, the abundances for different pigment endmember were re-calculated after removing the paper endmember. The experimental results show that the improved single-constant KM method has higher unmixing accuracy compared with the traditional spectral unmixing methods, and has good prospects for the application of unmixing composite pigments on the surface of Chinese paintings.
In this paper, we propose a simple but very effective approach in the presence of noisy labels. The memorization effects of Deep Neural Networks (DNNs) manifests that they first memorize training data with clean labels and memorize data with noisy labels gradually. Based on this phenomenon, we build Class Prediction Distributions(CPD) for each sample in the initial stage of network training. On the basis of CPD, we use our clean data selection strategy to divide training data into confidently clean data and noisy data. In this selection strategy, we rank the maximum value of CPD. Top-ranked samples are more likely to be clean samples. Finally, noisy labels classification is successfully achieved by using semi-supervised learning. Experiments on benchmark datasets including MNIST, Cifar-10, Cifar-100 and Clothing1M demonstrate that our approach can achieve a competitive performance.
Image classification is an important research branch in the field of computer vision. Ordinary image classification focuses on the discrimination of object differences, while fine-grained image classification focuses on the distinction between different sub-categories. Considering the small differences between sub-categories and the larger differences within sub-categories due to factors such as shooting angle, background, and posture, fine-grained image recognition becomes a challenging task. In order to solve the problem of fine-grained object classification, this paper proposes a fine-grained object classification method based on block diagonal feature and ensemble learning. The Gabor feature of the object image is block diagonalized by using low-rank recovery technology, and the low-rank feature representation of the object is constructed by introducing the block diagonal sparse regular term to increase the discriminability of features between object sub-categories. On this basis, the stacking ensemble learning method is used to classify objects, and a fine-grained real animal image and non-real animal image (toy animal image) classification database is constructed. And the algorithm design and experiment are completed. Through the proposed algorithm, accuracy of fine-grained classification of objects is improved.
Endometrial receptivity assessment based on the ultrasound image is a common and non-invasive way in clinician practice. Clinicians consider that the thickness of the endometrium is one of the most important assessment markers, which can be calculated with the endometrial region in ultrasound images. Suffering from low contrast of the boundaries in ultrasound images, it's a challenge that makes accurate segmentation of endometrial for thickness calculation. An automated assessment framework with a multi-task learning segmentation network is proposed in this paper. The VGG-based U-net is trained with an auxiliary pattern classification task, the losses of different tasks are combined by weighted sum based on uncertainty in the training phase. Experiment shows that the network has a more accurate prediction than single-task learning and the framework does a better thickness calculation.
