Object co-segmentation

Summary

In computer vision, object co-segmentation is a special case of , which is defined as jointly segmenting semantically similar objects in multiple images or video frames. Challenges It is often challenging to extract segmentation masks of a target/object from a noisy collection of images or video frames, which involves object discovery coupled with . A noisy collection implies that the object/target is present sporadically in a set of images or the object/target disappears intermittently throughout the video of interest. Early methods typically involve mid-level representations such as object proposals. Dynamic Markov networks-based methods A joint object discover and co-segmentation method based on coupled dynamic Markov networks has been proposed recently, which claims

Official source

https://en.wikipedia.org/wiki/Object_co-segmentation

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Video segmentation based on multiple features for interactive and automatic multimedia applications

In this thesis, a novel method for the segmentation of video sequences based on the analysis of multiple image features is presented. A key feature of the system is the distinction between two levels of segmentation, namely region and object segmentation. Regions are homogeneous areas of the images, which are extracted automatically by the computer. Semantically meaningful objects are obtained by grouping regions, automatically or through user interaction, according to the specific application. This splitting relieves the computer of ill-posed semantic problems, and allows a higher level of flexibility in the use of the results. The extraction of the regions is based the multidimensional analysis of several image features by a spatially constrained Fuzzy C-Means algorithm. The relative weighting of the different features is achieved by means of an adaptive system that takes into account the local level of reliability of each feature. The temporal tracking of the obtained regions is performed by means of a dual strategy in which the motion-compensated projection of the segmentation mask from previous frames is used to influence the segmentation of the current frame so as to achieve higher temporal coherence and stability.

EPFL1998

A MultiPath Network for Object Detection

Pedro Henrique Oliveira Pinheiro

The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a `MultiPath' network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66 overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges.

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Learning to Refine Object Segments

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge but are invariant to factors such as pose and appearance. In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach. The resulting bottom-up/top-down architecture is capable of efficiently generating high-fidelity object masks. Similarly to skip connections, our approach leverages features at all layers of the net. Unlike skip connections, our approach does not attempt to output independent predictions at each layer. Instead, we first output a coarse ‘mask encoding’ in a feedforward pass, then refine this mask encoding in a top-down pass utilizing features at successively lower layers. The approach is simple, fast, and effective. Building on the recent DeepMask network for generating object proposals, we show accuracy improvements of 10–20% in average recall for various setups. Additionally, by optimizing the overall network architecture, our approach, which we call SharpMask, is 50 % faster than the original DeepMask network (under .8 s per image).

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