Feature representation for statistical-learning-based object detection: A review

• We review the feature representation in statistical learning based object detection.
• We categorize and introduce features based on visual properties.
• The pros/cons on feature properties (e.g., descriptiveness, robustness) are discussed.
• Generic techniques such as dimension reduction and combination are introduced.
• We put some emphasis on future challenges in feature design through this review.

Statistical-learning-based object detection is an important topic in computer vision. It learns visual representation from annotated exemplars to identify semantic defined objects in images. High-performance object detection is usually carried out in feature space and effective feature representation can improve the performance significantly. Feature representation is the encoding process which maps raw image pixels inside local regions into discriminant feature space. The motivation of this paper is to present a review on feature representation in recent object detection methods. Visual features applied in object detection are categorized according to the differences in computation and visual properties. The most valued features are introduced and discussed in detail. Representative extensions are introduced briefly for comparison. Descriptive power, robustness, compactness as well as computational efficiency are viewed as important properties. According to these properties, discussions are presented on the advantages and drawbacks of features. Besides, generic techniques such as dimension reduction and combination are introduced. Through this review, we would like to draw the feature sketch and provide new insights for feature utilization, in order to tackle future challenges of object detection.