2 research outputs found
Density-Based Region Search with Arbitrary Shape for Object Localization
Region search is widely used for object localization. Typically, the region
search methods project the score of a classifier into an image plane, and then
search the region with the maximal score. The recently proposed region search
methods, such as efficient subwindow search and efficient region search, %which
localize objects from the score distribution on an image are much more
efficient than sliding window search. However, for some classifiers and tasks,
the projected scores are nearly all positive, and hence maximizing the score of
a region results in localizing nearly the entire images as objects, which is
meaningless.
In this paper, we observe that the large scores are mainly concentrated on or
around objects. Based on this observation, we propose a method, named level set
maximum-weight connected subgraph (LS-MWCS), which localizes objects with
arbitrary shapes by searching regions with the densest score rather than the
maximal score. The region density can be controlled by a parameter flexibly.
And we prove an important property of the proposed LS-MWCS, which guarantees
that the region with the densest score can be searched. Moreover, the LS-MWCS
can be efficiently optimized by belief propagation. The method is evaluated on
the problem of weakly-supervised object localization, and the quantitative
results demonstrate the superiorities of our LS-MWCS compared to other
state-of-the-art methods
Arbitrary-shape object localization using adaptive image grids
Sliding-window based search is a widely used technique for object localization. However, for objects of non-rectangle shapes, noises in windows may mislead the localization, causing unsatisfactory results. In this paper, we propose an efficient bottom-up approach for detecting arbitrary-shape objects using image grids as basic components. First, a test image is partitioned into n×n grids and the object is localized by finding a set of connected grids which maximize the classifier's response. Then, graph cut segmentation is used to improve the object boundary by utilizing local image context. Instead of using bounding boxes, the proposed approach searches connected regions of any shapes. With the graph cut refinement, our approach can start with coarse image grids and is robust to noises. To make image grids better cover the object of arbitrary shape, we also propose a fast adaptive grid partition method which takes image content into account and can be efficiently implemented by dynamic programming. The use of adaptive partition further improves the localization accuracy of our approach. Experiments on PASCAL VOC 2007 and VOC 2008 datasets demonstrate the effectiveness of our approach.Accepted versio