In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the gen- eration of object proposals. We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps. Instead, the first layers of the network can better localize the object of interest but with a reduced recall. Based on this observation we design a method for proposing object locations that is based on CNN features and that combines the best of both worlds. We build an inverse cascade that, going from the final to the initial convolutional layers of the CNN, selects the most promising object locations and refines their boxes in a coarse-to-fine manner. The method is efficient, because i) it uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals due to the inverse coarse-to-fine cascade. The method is also accurate; it outperforms most of the previously proposed object proposals approaches and when plugged into a CNN-based detector produces state-of-the- art detection performance.
An alternative approach to sliding-window methods is segmentation-based algorithms. This approach applies to the multiple levels of segmentation and then merge the generated segments in order to generate objects proposals @cite_18 @cite_5 @cite_19 @cite_8 . More specifically, selective search @cite_8 hierarchically aggregates multiple segmentations in a bottom-up greedy manner without involving any learning procedure, but based on low level cues, such as color and texture. Multiscale Combinatorial Grouping (MCG) @cite_18 extracts multiscale segmentations and merges them by using the edge strength in order to generate objects hypotheses. Carreira al @cite_5 propose to segment the object of interest based on graphcut. It produces segments from randomly generated seeds. As in selective search, each segment represents a proposal bounding box. Randomized Prim's @cite_19 uses the same segmentation strategy as selective search. However, instead of merging the segments in a greedy manner it learns the probabilities for merging, and uses those to speed up the procedure. Geodesic object proposals @cite_10 are based on classifiers that place seeds for a geodesic distance transform on an over-segmented image.
@cite_18 @cite_8 @cite_19 @cite_5 @cite_10
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We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five seconds per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals. This paper proposes an efficient and effective scheme to applying the sliding window approach popular in computer vision to 3D data. Specifically, the sparse nature of the problem is exploited via a voting scheme to enable a search through all putative object locations at any orientation. We prove that this voting scheme is mathematically equivalent to a convolution on a sparse feature grid and thus enables the processing, in full 3D, of any point cloud irrespective of the number of vantage points required to construct it. As such it is versatile enough to operate on data from popular 3D laser scanners such as a Velodyne as well as on 3D data obtained from increasingly popular push-broom configurations. Our approach is “embarrassingly parallelisable” and capable of processing a point cloud containing over 100K points at eight orientations in less than 0.5s. For the object classes car, pedestrian and bicyclist the resulting detector achieves best-in-class detection and timing performance relative to prior art on the KITTI dataset as well as compared to another existing 3D object detection approach. Hough voting methods efficiently handle the high complexity of multiscale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes. Interactive segmentation is useful for selecting objects of interest in images and continues to be a topic of much study. Methods that grow regions from foreground background seeds, such as the recent geodesic segmentation approach, avoid the boundary-length bias of graph-cut methods but have their own bias towards minimizing paths to the seeds, resulting in increased sensitivity to seed placement. The lack of edge modeling in geodesic or similar approaches limits their ability to precisely localize object boundaries, something at which graph-cut methods generally excel. This paper presents a method for combining geodesic-distance information with edge information in a graphcut optimization framework, leveraging the complementary strengths of each. Rather than a fixed combination we use the distinctiveness of the foreground background color models to predict the effectiveness of the geodesic distance term and adjust the weighting accordingly. We also introduce a spatially varying weighting that decreases the potential for shortcutting in object interiors while transferring greater control to the edge term for better localization near object boundaries. Results show our method is less prone to shortcutting than typical graph cut methods while being less sensitive to seed placement and better at edge localization than geodesic methods. This leads to increased segmentation accuracy and reduced effort on the part of the user.