Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes. It also can play an important role in improving object detection. In this work, we present two contextual models (rescoring and re-labeling models) that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the state-of-the-art RCNN-based object detection (Faster RCNN). We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO).
Context is defined as '' @cite_18 . We can add that contextual information is any data obtained from an object's own statistical property and or from its vicinity including intra-class and inter-class details.
@cite_18
2187027887 2125215748 1980661875 2145315825
The recent development of fast depth map fusion technique enables the realtime, detailed scene reconstruction using commodity depth camera, making the indoor scene understanding more possible than ever. To address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori. The key idea is that while contextual information could benefit the detection of either individual objects or object groups, both can contribute to object extraction when objects are unknown.Our method starts with a robust segmentation and partitions a subscene into segments, each of which represents either an independent object or a part of some object. A set of classifiers are trained for both individual objects and object groups, using a database of 3D scene models. We employ the multiple kernel learning (MKL) to learn per-category optimized classifiers for objects and object groups. Finally, we perform a graph matching to extract objects using the classifiers, thus grouping the segments into either an object or an object group. The output is an object-level labeled segmentation of the input subscene. Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes. Graphical abstractDisplay Omitted HighlightsTo address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts.our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing objects.The key idea is that while contextual information could benefit the detection of either individual objects or object groups, when object are unknown, both can contribute to object extraction. In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of exist ing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales. Context has been playing an increasingly important role in areas such as object detection, scene understanding, and image segmentation. Although many different types of contextual cues have been successfully explored, most of them only consider the pair-wise relationship between objects or parts. Several models utilize the high-order relationship for encoding contextual information. However, they mainly use a single contextual cue. In this paper, we present a novel high-order contextual descriptor (HOOD) to measure the strength of interactions among objects within an image. Heterogeneous contextual cues like semantic, spatial, and scale contexts are jointly integrated into HOOD to define the high-order interactions. The strength of these interactions are inferred by applying Bayes’ rule on the pure dependence of the involved objects. As a result, an object-level graph is constructed to represent the contextually consistent interactions. Moreover, we propose a HOOD based object localization framework to verify the effectiveness of HOOD. Experimental results on two benchmark datasets including SUN09 and PASCAL2007 show that our framework outperforms the state-of-the-art context based object localization methods. Finally, we apply HOOD on two multimedia applications: structured image retrieval and out-of-context object detection, which demonstrates the potential usages of HOOD. Highlights? Review of different sources of contextual information for object detection. ? New context model based on capturing support relationships and co-occurrences among objects. ? Evaluation of several context models on the SUN database. ? Introduction of a benchmark for detecting out-of-context objects. The context of an image encapsulates rich information about how natural scenes and objects are related to each other. Such contextual information has the potential to enable a coherent understanding of natural scenes and images. However, context models have been evaluated mostly based on the improvement of object recognition performance even though it is only one of many ways to exploit contextual information. In this paper, we present a new scene understanding problem for evaluating and applying context models. We are interested in finding scenes and objects that are "out-of-context". Detecting "out-of-context" objects and scenes is challenging because context violations can be detected only if the relationships between objects are carefully and precisely modeled. To address this problem, we evaluate different sources of context information, and present a graphical model that combines these sources. We show that physical support relationships between objects can provide useful contextual information for both object recognition and out-of-context detection.