Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.

Facial Expressions of Visitors in Forests along the Urbanization Gradient: What Can We Learn from Selfies on Social Networking Services?

People’s satisfaction towards the experience in forests is one of most important feedbacks that forest park managers need to meet positive visitors’ experiences. Although the drawbacks of questionnaire methodology are obvious for data collection from self-reported scores at the landscape scale, few alternative methods have been proposed. In this study, nine urban forest parks along the urbanization gradients in three capital cities of Northeast China were targeted to investigate their visitors’ selfies from social networking services (SNS) by assessing facial expressions. A total of 935 photos with location records were obtained from the SNS platform of Sina Micro-Blog in a social hot-event of ‘Golden Week Holidays of National Day of China’ of 2017. Images were recognized by FireFace software to assess scores of neutral, happy, sad, angry, surprised, scared, disgusted, and contempt expressions. Data were ranked in descending order and analyzed by Friedman’s test, correlation analysis, and Poisson regression. Visitors in downtown-forests showed fewer negative expressions at the most northern city than at the southern most one. The negative expressions tended to be alleviated with the increasing distance of forest parks from downtown. However, when the distance reached over 10 km no geographical effect was found. Female visitors showed positive emotional expressions to urban forests while male visitors showed no response. In conclusion, using data from SNS, this study found an experience in forest park less than 10 km from the downtown of a northern city resulted in female visitors showing the most positive expressions.

Machine Learning-Based Fast Angular Prediction Mode Decision Technique in Video Coding

In this paper we propose a machine learning-based fast intra-prediction mode decision algorithm, using random forest that is an ensemble model of randomized decision trees. The random forest is used to estimate an intra-prediction mode from a prediction unit and to reduce encoding time significantly by avoiding the intensive Rate-Distortion optimization of a number of intra-prediction modes. To this aim, we develop a randomized tree model including parameterized split functions at nodes to learn directional block-based features. The feature uses only four pixels reflecting a directional property of a block, and, thus the evaluation is fast and efficient. To integrate the proposed technique into the conventional video coding standard frameworks, the intra-prediction mode derived from the proposed technique, called an inferred mode (IM), is used to shrink the pool of the candidate modes before carrying out the Rate-Distortion (R-D) optimization. The proposed technique is implemented into the High Efficiency Video Coding Test Model (HM) reference software of the state-of-the-art video coding standard and Joint Exploration Model (JEM) reference software, by integrating the random forest trained off-line into the codecs. Experimental results demonstrate that the proposed technique achieves significant encoding time reduction with only slight coding loss as compared the reference software models.