Accurate semantic labeling of image pixels is difficult because intra-class variability is often greater than inter-class variability. In turn, fast semantic segmentation is hard because accurate models are usually too complicated to also run quickly at test-time. Our experience with building and running semantic segmentation systems has also shown a reasonably obvious bottleneck on model complexity, imposed by small training datasets. We therefore propose two simple complementary strategies that leverage context to give better semantic segmentation, while scaling up or down to train on different-sized datasets. As easy modifications for existing semantic segmentation algorithms, we introduce Decorrelated Semantic Texton Forests, and the Context Sensitive Image Level Prior. The proposed modifications are tested using a Semantic Texton Forest (STF) system, and the modifications are validated on two standard benchmark datasets, MSRC-21 and PascalVOC-2010. In Python based comparisons, our system is insignificantly slower than STF at test-time, yet produces superior semantic segmentations overall, with just push-button training.
@cite_21: In this paper we propose an approach to holistic scene understanding that reasons jointly about regions, location, class and spatial extent of objects, presence of a class in the image, as well as the scene type. Learning and inference in our model are efficient as we reason at the segment level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). Inference is done via a convergent message-passing algorithm, which, unlike graph-cuts inference, has no submodularity restrictions and does not require potential specific moves. We believe this is very important, as it allows us to encode our ideas and prior knowledge about the problem without the need to change the inference engine every time we introduce a new potential. Our approach outperforms the state-of-the-art on the MSRC-21 benchmark, while being much faster. Importantly, our holistic model is able to improve performance in all tasks.
@cite_17: We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, top-down figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16 relative) over our baselines on SDS, a 5 point boost (10 relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.
A sophisticated model was successfully demonstrated in @cite_21 , where the problems of semantic segmentation, object detection, and scene classification were cast as one holistic CRF model. Their parameters are learned via a structured learning algorithm, and inference is accomplished by a convergent message-passing algorithm. The model exploits various cues, such as scene type, co-occurrence statistics, the shape and location of the object, and different quantization levels to boost the segmentation result. In contrast, our proposed system exploits some of these important cues as context, but integrates them together with a much simpler model, achieving accuracy that approaches that of the more sophisticated model. Most recently, CNNs have also been exploited in a more sophisticated semantic segmentation framework @cite_17 . They compute feature vectors for each proposed region using two CNN's, trained especially on bounding boxes and free-form versions of the region. Thereafter, the concatenated feature vectors are passed through a linear SVM classifier to get a per-class potential. The final class label for each region is assigned via non-maximum suppression. Although, the system performed very well on the PascalVOC 2012 dataset, it did so at the expense of algorithm complexity.