text: Popular Hough Transform-based object detection approaches usually construct an appearance codebook by clustering local image features. However, how to choose appropriate values for the parameters used in the clustering step remains an open problem. Moreover, some popular histogram features extracted from overlapping image blocks may cause a high degree of redundancy and multicollinearity. In this paper, we propose a novel Hough Transform-based object detection approach. First, to address the above issues, we exploit a Bridge Partial Least Squares (BPLS) technique to establish context-encoded Hough Regression Models (HRMs), which are linear regression models that cast probabilistic Hough votes to predict object locations. BPLS is an efficient variant of Partial Least Squares (PLS). PLS-based regression techniques (including BPLS) can reduce the redundancy and eliminate the multicollinearity of a feature set. And the appropriate value of the only parameter used in PLS (i.e., the number of latent components) can be determined by using a cross-validation procedure. Second, to efficiently handle object scale changes, we propose a novel multi-scale voting scheme. In this scheme, multiple Hough images corresponding to multiple object scales can be obtained simultaneously. Third, an object in a test image may correspond to multiple true and false positive hypotheses at different scales. Based on the proposed multi-scale voting scheme, a principled strategy is proposed to fuse hypotheses to reduce false positives by evaluating normalized pointwise mutual information between hypotheses. In the experiments, we also compare the proposed HRM approach with its several variants to evaluate the influences of its components on its performance. Experimental results show that the proposed HRM approach has achieved desirable performances on popular benchmark datasets.
  @cite_33: A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the kernel partial least squares (PLS) regression model. Similar to principal components regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS creates the components by modeling the relationship between input and output variables while maintaining most of the information in the input variables. PLS is useful in situations where the number of explanatory variables exceeds the number of observations and or a high level of multicollinearity among those variables is assumed. Motivated by this fact we will provide a kernel PLS algorithm for construction of nonlinear regression models in possibly high-dimensional feature spaces.We give the theoretical description of the kernel PLS algorithm and we experimentally compare the algorithm with the existing kernel PCR and kernel ridge regression techniques. We will demonstrate that on the data sets employed kernel PLS achieves the same results as kernel PCR but uses significantly fewer, qualitatively different components.
  @cite_8: 
  @cite_28: We evaluate the robustness of five regression techniques for monocular 3D pose estimation. While most of the discriminative pose estimation methods focus on overcoming the fundamental problem of insufficient training data, we are interested in characterizing performance improvement for increasingly large training sets. Commercially available rendering software allows us to efficiently generate large numbers of realistic images of poses from diverse actions. Inspired by recent work in human detection [20], we apply PLS and kPLS regression to pose estimation. We observe that kPLS regression incrementally approximates GP regression using the strongest nonlinear correlations between image features and pose. This provides robustness, and our experiments show kPLS regression is more robust than two GP-based state-of-the-art methods for pose estimation.
  @cite_29: Due to the large amount of data to be processed by visual applications aiming at extracting high-level understanding of the scene, low-level methods such as object detection are required to have not only high accuracy but also low computational cost in order to provide fast and reliable information. Training sets containing samples representing multiple scenes are used to learn object detectors that can be reliably used in different scenarios. In general, information extracted from multiple feature channels is combined to capture the large variability present in these different environments. Although this approach provides accurate detection results, it usually leads to a high computational cost. On the other hand, if characteristics of the scene are known before-hand, a set of simple and fast computing features might be sufficient to provide high accuracy at a low computational cost. Therefore, it is valuable to seek a balance between these two extremes such that the detection method not only works well in different scenarios but also is able to extract enough information from a scene. We integrate a set of data-driven regression models with a multi-stage based human detection method trained to be used in different environments. The regressions are used to estimate the detector response at each stage and the location of the objects. The use of the regression models allows the method to reject large number of detection windows quickly. Experimental results based on human detection show that the addition of the regression models reduces the computational cost by as much as ten times with very small or no degradation on detection accuracy.
  @cite_32: We propose a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art. Our new features are built on the basis of low-level visual features and spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. We then directly optimise the partial area under the ROC curve (pAUC) measure, which concentrates detection performance in the range of most practical importance. The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets. We advance state-of-the-art results by lowering the average miss rate from 13 to 11 on the INRIA benchmark, 41 to 37 on the ETH benchmark, 51 to 42 on the TUD-Brussels benchmark and 36 to 29 on the Caltech-USA benchmark.
  @cite_3: Human age estimation has recently become an active research topic in computer vision and pattern recognition, because of many potential applications in reality. In this paper we propose to use the kernel partial least squares (KPLS) regression for age estimation. The KPLS (or linear PLS) method has several advantages over previous approaches: (1) the KPLS can reduce feature dimensionality and learn the aging function simultaneously in a single learning framework, instead of performing each task separately using different techniques; (2) the KPLS can find a small number of latent variables, e.g., 20, to project thousands of features into a very low-dimensional subspace, which may have great impact on real-time applications; and (3) the KPLS regression has an output vector that can contain multiple labels, so that several related problems, e.g., age estimation, gender classification, and ethnicity estimation can be solved altogether. This is the first time that the kernel PLS method is introduced and applied to solve a regression problem in computer vision with high accuracy. Experimental results on a very large database show that the KPLS is significantly better than the popular SVM method, and outperform the state-of-the-art approaches in human age estimation.
  @cite_13: Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors.
  @cite_25: Significant research has been devoted to detecting people in images and videos. In this paper we describe a human detection method that augments widely used edge-based features with texture and color information, providing us with a much richer descriptor set. This augmentation results in an extremely high-dimensional feature space (more than 170,000 dimensions). In such high-dimensional spaces, classical machine learning algorithms such as SVMs are nearly intractable with respect to training. Furthermore, the number of training samples is much smaller than the dimensionality of the feature space, by at least an order of magnitude. Finally, the extraction of features from a densely sampled grid structure leads to a high degree of multicollinearity. To circumvent these data characteristics, we employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions, reduced from the original 170,000). Our human detection system, employing PLS analysis over the enriched descriptor set, is shown to outperform state-of-the-art techniques on three varied datasets including the popular INRIA pedestrian dataset, the low-resolution gray-scale DaimlerChrysler pedestrian dataset, and the ETHZ pedestrian dataset consisting of full-length videos of crowded scenes.
  @cite_12: Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and their surroundings, along with the Histograms of Oriented Gradients feature and a simple yet powerful image descriptor that captures the structural characteristics of objects named Pairs of Pixels. The combination of these features leads to an extremely high-dimensional feature set (approximately 70,000 elements). Partial Least Squares is first used to project the data onto a much lower dimensional subspace. Then, a powerful feature selection analysis is employed to improve the performance while vastly reducing the number of features that must be calculated. We compare our system to previous approaches on two challenging data sets and show superior performance.
summary: The Partial Least Squares technique @cite_8 is a supervised dimensional reduction tool, which is usually followed by a feature selection step to discard noisy and redundant features. For instance, @cite_25 and @cite_12 use PLS followed by a feature selection strategy named Ordered Predictors Selection to detect humans and vehicles on challenging datasets, respectively. Recently, a novel pedestrian detection approach @cite_32 performs feature selection by using an Adaboost algorithm to select spatially pooled covariance matrix features and LBP features. By intensively inspecting the experimental design of the detector, the approach proposed in @cite_32 obtains an impressive performance in pedestrian detection. @cite_29 employs PLS in a multi-stage framework to perform data-driven object detection. Furthermore, a non-linear variant of PLS in Reproducing Kernel Hilbert Space, i.e. Kernel Partial Least Squares @cite_33 , is employed in @cite_13 @cite_28 @cite_3 to improve the performance of PLS in the tasks of head pose estimation, monocular 3D pose estimation and human age estimation, respectively.