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In response to the growth of digital photography and its many related applications, researchers have been actively investigating methods for providing automated aesthetical evaluation and classification of photographs. For computational networks to recognize aesthetic qualities, the learning algorithms must be trained using sample sets of characteristics that have known aesthetic values. Traditional methods for developing this training have required manual extraction of aesthetic features for use in the practice datasets. With abundant appearance of convolutional neural networks (CNN), the networks have learned features automatically and have acted as important tools for evaluation and classification. At the time of our research, several existing convolutional neural networks for photograph aesthetical classification only used shallow depth networks, which limit the improvement of performance. In addition, most methods have extracted only one patch as a training sample, such as a down-sized crop from each image. However, a single patch might not represent the entire image accurately, which could cause ambiguity during training. What's more, for existing datasets, the numbers of high quality images of each category are mostly too small to train deep CNN networks. To solve these problems, we introduce a novel photograph aesthetic classifier with a deep and wide CNN for fine-granularity aesthetical quality prediction. First, we download a large number of consumer photographic images from DPChallenge.com (a well-known online photography portal) to construct a dataset suitable for aesthetic quality assessment. Then, we zoom out the images into 256×256 by bilinear interpolation and crop 10 patches (Center+four Corners+Flipping). Once we have associated the set with the image's training labels, we feed the images with the bag of patches into the fine-tuned networks. Our proposed computational method is configured to classify the photographs into high and low aesthetic values. A training pattern specifying an output of (0, 1) indicates that the corresponding image belongs to the “low aesthetic quality” set. Likewise, a training pattern with an output of (1, 0) indicates that the corresponding image belongs to the “high aesthetic quality” set. Experimental results show that the accuracy of classification provided by our method is greater than 87.10%, which is noticeably better than the state-of-the-art methods. In addition, our experiments show that our results are fundamentally consistent with human visual perception and aesthetic judgments.

Photograph aesthetical evaluation has been widely investigated in these decades. For fine-granularity aesthetic quality prediction, a novel aesthetics classifier based on improved artificial neural network combined with an Autoencoder technique is presented. First, we download large consumer photographic images from a well-known online photograph portal. Then, we extract 56 features normalized to 0–1 and train the networks with photographs of high and low ratings to test the quality of photos. Experimental results show that the accuracy of classification is above 86.67%, which is better than all state-of-the-art methods. Meanwhile, it is observed from experiments that the extracted features are consistent with the humans׳ visual perception systems.

This paper addresses the observer-based l 2 - l ∞ control problem for a class of nonhomogeneous Markov jump Lur?e systems subject to sensor saturations in discrete-time domain. The time-varying characteristic of transition probabilities is considered as a polytope description. The saturations occurred in the sensor outputs are handled by a decomposition approach. By constructing a stochastic multiple Lyapunov function, sufficient conditions for the existence of an observer-based controller with nonlinear feedback terms are derived, such that the closed-loop systems are stochastically stable and satisfy a given l 2 - l ∞ performance index. A linear matrix inequality approach is presented for designing such an observer-based l 2 - l ∞ controller. Finally, a numerical example is presented to illustrate the effectiveness and potential of the developed theoretical results. HighlightsThe observer-based l 2 - l ∞ controller is designed for a class of Markov jump Lur?e systems subject to sensor saturations.Sensor saturations are decomposed into a linear term and a nonlinear term satisfying a sector condition.Lyapunov function contains three quadratic functions of the estimation error,observer states and nonlinearities.The explicit expressions of desired gain matrices for the designed observer and controller are obtained.