This invention relates to image segmentation for large-scale fine-grained recognition.
Certain tasks require recognition of detailed objects. For example, the task of automatic recognition of different species of flowers can be classified as subcategory recognition, or fine-grained classification, in which the base-level category is ‘flower’ and the classes to be recognized are different types of flowers. In the subcategory recognition setting, the main challenge lies in the very fine differences between possibly similar objects that belong to different classes. Only very well trained experts are able to discriminate between all of the categories properly. Naturally, an automatic recognition system in such a setting will provide much value to non-experts.
One of the main goals for any such system is improving the recognition performance. As mentioned, the main challenges in subcategory classification are the fine differences between classes. Other challenges, specific to an automatic recognition system, are also present, for example, scale variations, intra-class variability, inter-class similarities, image blur, among others, as experienced by a conventional system such as that of FIG. 1. In FIG. 1 the system can segment potential object that belongs to the super-category. The system can also utilize the segmented image in a combined pipeline for better performance. The process applies a dense grid descriptor Histogram of Oriented Gradients (HOG) (1). Next, a local coordinate super-vector 2 is coded. An Spatial Pyramid Matching (SPM) pooling is done (4), and a linear support vector machine (SVM) is applied (6).
One complication for images of flowers is that flower photographs are often taken in natural settings with rich and challenging backgrounds. Although the background can generally provide useful context, it can sometimes serve as distractor to a classification technique. For example, background features can become prominent and be extracted as possibly good discriminators, or some background features may be matched across different categories and thus make it harder to discriminate among them. This can cause deteriorated performance of the classification technique.