Contextual Object Detection Using Set-Based Classification

Download

index.pdf

Date

2012-01-01

Author

Cinbiş, Ramazan Gökberk

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

193
views

0
downloads

We propose a new model for object detection that is based on set representations of the contextual elements. In this formulation, relative spatial locations and relative scores between pairs of detections are considered as sets of unordered items. Directly training classification models on sets of unordered items, where each set can have varying cardinality can be difficult. In order to overcome this problem, we propose SetBoost, a discriminative learning algorithm for building set classifiers. The SetBoost classifiers are trained to rescore detected objects based on object-object and object-scene context. Our method is able to discover composite relationships, as well as intra-class and inter-class spatial relationships between objects. The experimental evidence shows that our set-based formulation performs comparable to or better than existing contextual methods on the SUN and the VOC 2007 benchmark datasets.

Subject Keywords

Average precision, Object class, Context model, Reference object, Contextual relationship

URI

https://hdl.handle.net/11511/56664

DOI

https://doi.org/10.1007/978-3-642-33783-3_4

Conference Name

12th European Conference on Computer Vision (ECCV)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Rescoring detections based on contextual scores in object detection Zorlu, Ersan Vural; Akbaş, Emre; Department of Computer Engineering (2019) To detect objects in an image, current state-of-the-art object detectors firstly definecandidate object locations, and then classify each of them into one of the predefinedcategories or as background. They do so by using the visual features extracted locallyfrom the candidate locations; omitting the rich contextual information embedded inthe whole image. Contextual information can be utilized to complement the informa-tion extracted locally and thereby to improve object detection accuracy. Researchershave p...
Segmentation Driven Object Detection with Fisher Vectors Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2013-01-01) We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks...
Utilization of dense depth information for monoview object detection and instance segmentation Çakırgöz, Çağlayan Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2022-5-10) Object detection aims for detecting objects of certain classes in an image by bounding them in rectangular boxes whereas instance segmentation tries to detect objects in pixel level. Deep learning techniques, which have shown great improvements over the last decade, are utilized in these topics as well, and a significant success is achieved against the traditional methods. Similar improvements can be observed in dense depth estimation which deals with deducing dense information of a scene from a single imag...
Object Augmentation for Out-of-Context Object Recognition Eryüksel, Oğul Can; Kalkan, Sinan; Department of Computer Engineering (2022-5-18) The visual context in an image contains rich information about and between foreground objects and the background. Deep learning models learn contextual information implicitly in general. However, since training datasets generally do not include all possible contexts, deep models tend to memorize contextual details. This can lead to poor recognition performance when models are deployed in real-world applications since objects may appear in unexpected contexts or places. These types of objects are called out-...
Object Detection with Convolutional Context Features Kaya, Emre Can; Alatan, Abdullah Aydın (2017-01-01) A novel extension to Huh B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection ben...

Citation Formats

R. G. Cinbiş, “Contextual Object Detection Using Set-Based Classification,” Florence, ITALY, 2012, vol. 7577, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56664.