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Abstract Prohexadione-Calcium (Pro-Ca) plays key roles in improving fruit quality and yield by regulating various aspects of plant growth. However, the effects of how Pro-Ca regulates the regulation of sugar and acid balance and its impact on the production of volatile aroma substances during fruit growth and development are poorly understood. In this study, the Pro-Ca solutions developed at concentrations of 200, 400, 600 and 800 mg·L-1 were sprayed on the entire “Chardonnay” grape tree 22, 42, 62 and 82 days after initial flowering. The values of endogenous hormones, sugar and acid content, enzyme activities and flavor content were then measured in grapes 45, 65, 85 and 105 days (ripeness stage) after the initial flowering. The results showed that Pro-Ca had significant effects on fruits during development, including reducing ABA content, increasing ZT, GA3 and IAA levels, promoting fruit ripening and enhancing enzymes, which are involved in sugar and acid synthesis. Consequently, these effects led to an increase in sugar and acid content in the berries. Particularly during the ripening phase, the application of 600 mg L-1 Pro-Ca resulted in an increase in soluble sugar content of 11.28% and a significant increase in citric acid and malic acid content of 97.80% and 68.86%, respectively. Additionally, Pro-Ca treatment enhanced both the variety and quantity of aroma compounds present in the berries, with the 600 mg·L-1 Pro-Ca treatment showcasing the most favorable impact on volatile aroma compounds in ‘Chardonnay’ grapes. The levels of aldehydes, esters, alcohols, phenols, acids, ketones, and terpenes were significantly higher under the 600 mg·L-1 Pro-Ca treatment compared to those of control with 51.46 − 423.85% increase. In conclusion, Pro-Ca can regulate the content of endogenous hormones and the activities of enzymes related to sugar and acid metabolism in fruit, thereby increasing the content of soluble sugar and organic acid in fruit and the diversity and concentration of fruit aroma substances. Among them, foliar spraying 600 mg · L-1 Pro-Ca has the best effect. In the future, we need to further understand the molecular mechanism of Pro-Ca in grape fruit to lay a solid foundation for quality improvement breeding.

View variation is a major challenge in face recognition. In this study, the authors propose a novel cross‐view face recognition method by seeking potential intermediate domains between the source and target views to model the connection of varying‐views faces. Specifically, each intermediate domain is associated with a dictionary subspace. Learning proceeds in two phases. First, the authors discriminatively train a sub‐dictionary for each subclass of data, which then compose a structured dictionary of powerful reconstructive and discriminative capability on the source data. Secondly, the authors gradually adapt the source domain dictionary to the target domain by incrementally reducing the reconstruction error on the target data, which forms a smooth transition path connecting the source and target domains. Instead of updating the structured dictionary integrally, the authors develop a refined sub‐dictionary‐based updating algorithm, which makes the intermediate dictionaries fit on the target data better and faster. Finally, the authors apply invariant sparse codes across the source, intermediate and target domains to render domain‐shared representations, where the sample differences caused by view changes are reduced. Experiments on the CMU‐PIE and Multi‐PIE dataset demonstrate the effectiveness of the proposed method.

Text information contained in scene images is very helpful for high‐level image understanding. In this study, the authors propose to learn co‐occurrence of local strokes for scene text recognition by using a spatiality embedded dictionary (SED). Unlike spatial pyramid partitioning images into grids to incorporate spatial information, the authors SED associates every codeword with a particular response region and introduces more precise spatial information for robust character recognition. After localised soft coding and max pooling of the first layer, a sparse dictionary is learned to model co‐occurrence of several local strokes, which further improves classification performance. Experimental results on two scene character recognition datasets ICDAR2003 and CHARS74 K demonstrate that their character recognition method outperforms state‐of‐the‐art methods. Besides, competitive word recognition results are also reported for four benchmark word recognition datasets ICDAR2003, ICDAR2011, ICDAR2013 and street view text when combining their character recognition method with a conditional random field language model.

Pooling strategies, such as max pooling and sum pooling, have been widely used to obtain the global representations for action videos. However, these pooling strategies have several disadvantages. First, they are easily affected by unwanted background local features, the absence of discriminative local features and the times of actions periodically performed by actors. Second, most pooling strategies only use local features to build the global representation that captures little mid‐level features for action representation. In this study, the authors propose a novel weighted pooling strategy based on actionlets representation for action recognition. The actionlets are defined as the movements of large bodies such as legs, arms and head, which capture rich mid‐level features for action representation. Besides, the authors’ method also incorporates the distribution information of actionlets into pooling procedure. Specifically, a pooling weight, which determines the importance of actionlet on the final video representation, is assigned to each actionlet. To learn the weight, they propose a novel discriminative learning algorithm to capture the discriminative information for pooling operation. They evaluate their weighted pooling on three datasets: KTH actions dataset, UCF sports dataset and Youtube actions dataset. Experimental results show the effectiveness of the proposed method.