Multi-Resolution Cascades for Multiclass Object Detection
An algorithm for learning fast multiclass object detection cascades is introduced. It produces multi-resolution (MRes) cascades, whose early stages are binary target vs. non-target detectors that eliminate false positives, late stages multiclass classifiers that finely discriminate target classes, and middle stages have intermediate numbers of classes, determined in a data-driven manner. This MRes structure is achieved with a new structurally biased boosting algorithm (SBBoost). SBBost extends previous multiclass boosting approaches, whose boosting mechanisms are shown to implement two complementary data-driven biases: 1) the standard bias towards examples difficult to classify, and 2) a bias towards difficult classes. It is shown that structural biases can be implemented by generalizing this class-based bias, so as to encourage the desired MRes structure. This is accomplished through a generalized definition of multiclass margin, which includes a set of bias parameters. SBBoost is a boosting algorithm for maximization of this margin. It can also be interpreted as standard multiclass boosting algorithm augmented with margin thresholds or a cost-sensitive boosting algorithm with costs defined by the bias parameters. A stage adaptive bias policy is then introduced to determine bias parameters in a data driven manner. This is shown to produce MRes cascades that have high detection rate and are computationally efficient. Experiments on multiclass object detection show improved performance over previous solutions.
This paper proposes a new cascading system for multiclass object detection. The algorithm exploits a new multiclass structurally biased boosting algorithm (SBBoost) that allows to introduce a bias towards some specific classes. SBBoost is used to implement a cascade where first models are biased towards the elimination of false positives (ie., the good detection of the negative class), late models are biased to finely discriminate the target classes, and middle models are biased in-between. The proposed MRes cascade is shown to outperform other cascades models on two object detection problems.The problem of multiclass object detection is important and has many applications. Building cascades for this setting is also very interesting to minimize detection times. Although several solutions already exist for this problem, the proposed method is original in that it solves the problem in a more integrated way than existing mutliclass cascading systems. A second contribution of the paper is the introduction of a new cost-sensitive multiclass boosting algorithm, called SBBoost. This algorithm seems also original but a comparison with related works on cost-sensitive multiclass boosting is missing in the paper (which focuses on the cascade aspect).The derivation of the SBBoosting method from the MCBoost method of Saberian and Vasconcelos is sound. It is first shown that MCBoost naturally introduces a bias towards difficult classes and this feature is exploited to introduce artificial class biases with the introduction of new bias parameters into the margin definition. Interestingly, these parameters can be interpreted as the logs of the cost of assigning examples from one class to another.The derivation of the MRes cascade model makes also much sense. I like the idea of going progressively from target detection to the discrimination of the classes. The proposed stage adaptive bias policy should clearly achieve this goal but it appears however to be not very well motivated from a theoretical point view. It is not clear which global criterion it optimizes and whether other policies could not achieve better performance.Experiments clearly show that the MRes cascades outperform other cascades on two different object detection problems. The compared methods are however rather complex and depends on several parameters that are not given in the paper. For this reason, while results of MRes are very good, we can only trust the authors that the other methods have been tuned appropriately for the comparison. With the information provided in the paper, it is clearly impossible to reproduce the experiments.I’m also disappointed that the proposed method is only evaluated as a whole. There is no analysis of the effect of its parameters or evaluation of the different design choices. For example, it would be interesting to turn off the stage adaptive bias policy to see how important it is to achieve good performance. What is the performance achieved by the MCE method without adaption? The newly proposed SBBoost method is also not validated per se. It would be interesting to compare it with other cost-sensitive multiclass boosting methods. Is this method crucial in the context of the cascade or could it be replaced by another cost-sensitive approach with similar performance?The paper is well written. It is mostly clear and well organized. While all individual steps of the algorithm are clear, it is not so clear however how all pieces are put together. It should help to have a pseudo-code description of the method. For example, while I think I understand how the cascade is trained, I’m not sure how exactly it is used to compute predictions. In Equation (1), what are the h_i(x) models? Is h_i(x) the ith weak learner or does it correspond to the sum of the predictions from all weak learners until the ith one? I’m not sure also to understand how the update of the beta is interleaved with the adaptive policy of (22).Minor comments:- The conclusion is not very useful as it is a simple summary of the paper without any discussion of the method or of future works.- Page 5,  « \beta is increased to (\beta+1)/2 » -> decreased