Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether analysing leaf patterns can provide sufficient information for further differentiating cultivars. This paper reports our attempt on cultivar recognition from leaves as a general very fine-grained pattern recognition problem, which is not only a challenging research problem but also important for cultivar evaluation, selection and production in agriculture. We propose a novel local R-symmetry co-occurrence method for characterising discriminative local symmetry patterns to distinguish subtle differences among cultivars. Through scalable and moving R-relation radius pairs, we generate a set of radius symmetry co-occurrence matrices (RsCoM) and their measures for describing the local symmetry properties of interior regions. By varying the size of the radius pair, the RsCoM measures local R-symmetry co-occurrence from coarse to fine scales. We constructed three leaf image databases, SoyCultivar, CottCultivar, and PeanCultivar, for an extensive experimental evaluation on recognition across soybean, cotton and peanut cultivars. Encouraging experimental results of the proposed method in comparison with the state-of-the-art leaf species recognition methods demonstrate the effectiveness of the proposed method for cultivar identification, which may advance the research in leaf recognition from species to cultivar.

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.