Abstract
Image aesthetic evaluation refers to the subjective aesthetic evaluation of images. Computational aesthetics has been widely concerned due to the limitations of subjective evaluation. Aiming at the problem that the existing evaluation methods of image aesthetic quality only extract the low-level features of images and they have a low correlation with human subjective perception, this paper proposes an aesthetic evaluation model based on latent semantic features. The aesthetic features of images are extracted by superpixel segmentation that is based on weighted density POI (Point of Interest), which includes semantic features, texture features, and color features. These features are mapped to feature words by LLC (Locality-constrained Linear Coding) and, furthermore, latent semantic features are extracted using the LDA (Latent Dirichlet Allocation). Finally, the SVM classifier is used to establish the classification prediction model of image aesthetics. The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%.
Highlights
With the development of mobile internet technology, there are millions of online pictures uploaded and shared every day
The experimental results on the AVA dataset show that the feature coding based on latent semantics proposed in this paper improves the adaptability of the image aesthetic prediction model, and the correlation with human subjective perception reaches 83.75%
The LDA algorithm obtains the subject vocabulary of the feature document, and the similarity between the statistical feature word and topic vocabulary leads to the latent semantic feature
Summary
With the development of mobile internet technology, there are millions of online pictures uploaded and shared every day. Two coding schemes are performed on the extracted features: the first scheme is the LLC coding, which encodes the extracted features into semantic features containing image aesthetic information; the second scheme is to further quantize the extracted features, map them into feature words, and utilize the LDA (Latent Dirichlet Allocation) model [14] to extract the common features of aesthetic images—latent semantic features; in the AVA dataset [15], the machine learning algorithm is used to perform the feature combination optimization, and the optimal aesthetic evaluation classification model is obtained. The superpixel block algorithm that is based on the weighted density of POI is designed to extract local handcraft features of the image. The density of POI measures the complexity of local areas It increases the relationship between image features and aesthetic complexity attributes.
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