This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that outperform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.
Without being interested in jigsaw puzzle solving, Doersch et al. proposed a deep neural network to predict the relative position of two adjacent fragments in @cite_22 . The end goal of the authors is to use this task as a pretraining step of deep convolutional neural network (CNN), harnessing the vast amounts of unlabeled images since the ground truth for such task can be automatically generated. The intuitions for training features able to predict their context are the same as what is found in the text literature with word2vec @cite_8 or skip-thought @cite_7 . In @cite_22 , the authors show their proposed task outperforms all other unsupervised pretraining methods. Based on @cite_22 , Noroozi and Favaro @cite_25 introduce a network that compares all the nine tiles at the same time. They claim that the complete representation obtained allows discarding the ambiguities that may have been learned with the algorithm proposed by Doersch et al. Gur et al. @cite_19 consider missing fragments, but heavily rely on border to solve the puzzle.
@cite_22 @cite_7 @cite_8 @cite_19 @cite_25
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This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-the-art performance among algorithms which use only Pascal-provided training set annotations. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. Research into computational jigsaw puzzle solving, an emerging theoretical problem with numerous applications, has focused in recent years on puzzles that constitute square pieces only. In this paper we wish to extend the scientific scope of appearance-based puzzle solving and consider ’’brick wall” jigsaw puzzles – rectangular pieces who may have different sizes, and could be placed next to each other at arbitrary offset along their abutting edge – a more explicit configuration with properties of real world puzzles. We present the new challenges that arise in brick wall puzzles and address them in two stages. First we concentrate on the reconstruction of the puzzle (with or without missing pieces) assuming an oracle for offset assignments. We show that despite the increased complexity of the problem, under these conditions performance can be made comparable to the state-of-the-art in solving the simpler square piece puzzles, and thereby argue that solving brick wall puzzles may be reduced to finding the correct offset between two neighboring pieces. We then move on to focus on implementing the oracle computationally using a mixture of dissimilarity metrics and correlation matching. We show results on various brick wall puzzles and discuss how our work may start a new research path for the puzzle solving community. We propose a novel unsupervised learning approach to build features suitable for object detection and classification. The features are pre-trained on a large dataset without human annotation and later transferred via fine-tuning on a different, smaller and labeled dataset. The pre-training consists of solving jigsaw puzzles of natural images. To facilitate the transfer of features to other tasks, we introduce the context-free network (CFN), a siamese-ennead convolutional neural network. The features correspond to the columns of the CFN and they process image tiles independently (i.e., free of context). The later layers of the CFN then use the features to identify their geometric arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. We pre-train the CFN on the training set of the ILSVRC2012 dataset and transfer the features on the combined training and validation set of Pascal VOC 2007 for object detection (via fast RCNN) and classification. These features outperform all current unsupervised features with (51.8 , ) for detection and (68.6 , ) for classification, and reduce the gap with supervised learning ( (56.5 , ) and (78.2 , ) respectively).