Interactive image segmentation is an important computer vision problem that has numerous real world applications. Models for image segmentation are generally trained to minimize the Hamming error in pixel labeling. The Hamming loss does not ensure that the topology/structure of the object being segmented is preserved and therefore is not a strong indicator of the quality of the segmentation as perceived by users. However, it is still ubiquitously used for training models because it decomposes over pixels and thus enables efficient learning. In this paper, we propose the use of a novel family of higher-order loss functions that encourage segmentations whose layout is similar to the ground-truth segmentation. Unlike the Hamming loss, these loss functions do not decompose over pixels and therefore cannot be directly used for loss-augmented inference. We show how our loss functions can be transformed to allow efficient learning and demonstrate the effectiveness of our method on a challenging segmentation dataset and validate the results using a user study. Our experimental results reveal that training with our layout-aware loss functions results in better segmentations that are preferred by users over segmentations obtained using conventional loss functions.
The Shape Boltzmann Machine (SBM) and its multilabel version MSBM have been recently introduced as deep generative models that capture the variations of an object shape. While being more flexible MSBM requires datasets with labeled parts of the objects for training. In the paper we present an algorithm for training MSBM using binary masks of objects and the seeds which approximately correspond to the locations of objects parts. The latter can be obtained from part-based detectors in an unsupervised manner. We derive a latent variable model and an EM-like training procedure for adjusting the weights of MSBM using a deep learning framework. We show that the model trained by our method outperforms SBM in the tasks related to binary shapes and is very close to the original MSBM in terms of quality of multilabel shapes.
Parameters that affect the perception quality of visual data has been investigated. Evaluation of such parameters due to distortion during filtering was determined. Segmentation methods according to colour and brightness similarity were discussed. Perceptive model for contrast sensitivity influence evaluation was discussed. The image region detection method for watermarking is suggested.
In this paper we consider the Shape Boltzmann Machine(SBM) and its multi-label version MSBM. We present an algorithm for training MSBM using only binary masks of objects and the seeds which approximately correspond to the locations of objects parts.
We discuss a model for image segmentation that is able to overcome the short-boundary bias observed in standard pairwise random field based approaches. To wit, we show that a random field with multi-layered hidden units can encode boundary preserving higher order potentials such as the ones used in the cooperative cuts model of  while still allowing for fast and exact MAP inference. Exact inference allows our model to outperform previous image segmentation methods, and to see the true effect of coupling graph edges. Finally, our model can be easily extended to handle segmentation instances with multiple labels, for which it yields promising results.