## 2 Related Work
### Object Recognition  
Recently, researchers have achieved quite good results for the recognition of individual objects [67; 68] and object classes, for example, in the Pascal VOC challenge [69]. Best practice approaches are based on visual features that are generated from local region detector-descriptor chains [68; 70] and from object models that are suited for recognition with feature matching or machine learning approaches [71]. In the context of object recognition, good visual features should generally compute the same values when they are applied to the same objects and distinct values for different objects. It is well established that feature types mainly differ by the trade-off that they achieve between their discriminative power and invariance. Furthermore, different recognition tasks require different trade-offs, and thus no single visual feature is optimal in all situations [72]. In addition to visual features, [73] has shown that all components of a recognition approach can have strong influences on the achieved results. Popular approaches include interest point and region detectors [74] (Difference of Gaussian points, Harris-Laplace corners, MSER regions), segmentation-based approaches [75], dense sampled regions [76], and even global approaches can be very efficient for some tasks [77]. Texture-based features (SIFT [67], SURF [68], HoG [78]) are mostly used to describe these interest regions while color-based and shape-based features have been of minor interest in the last years. However, the success of combined features that incorporate color and texture [76] indicate the importance of all feature types. In a further processing step, visual features are then used to form object models such as the popular bag-of-features [79] and sparse representations [80]. The authors of [81] pointed out that the manual process of choosing appropriate algorithms and tuning them for a given task is more an art than a science. A couple of works investigated the automatic selection and customization of recognition approaches from different directions: Attempts to optimize the parameters of specific visual features, like SIFT or HOG, are given in [82; 83; 84]. [PERSON] _et al_. [72] proposes a kernel-learning approach to select the best feature combination for a task using a SVM framework that works with all types of features. [85] uses a convolutional neural network to learn new features for each task instead of using manually designed, hand-crafted features while [81] proposed a trainable local feature matching approach that uses a boosting framework. In contrast to these works, we try to automate the simultaneous selection and customization of the entire recognition process with all kinds of visual features, matching strategies, and parameter settings. To the best knowledge of the author, no work exists that presents such a holistic approach for the selection of object recognition approaches in video. Similar to the mentioned works, we select recognition approaches with the help of initially selected training data. Although it might be possible to tweak algorithms manually for specific tasks and objects, it is much easier and more intuitive to provide appropriate training data instead [81]. According to [82], these data-driven approaches further increase the probability to learn the invariance accurately for specific applications. A potential drawback of data-driven systems is the requirement of training data that is usually human labeled [86; 87] or synthetically generated by artificial image transformations [85]. The manual annotation of training data is time consuming and synthetic approaches often do not capture all the invariance of real data. Thus, [74; 82] used optical flow tracking as a data collection step to customize the recognition of moving objects without any human intervention. We propose an iterative data collection approach where only one keyframe that contains an object example has to be selected by the user in the first place.