In this paper we study the problem of image representation learning without human annotation. By following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection. To maintain the compatibility across tasks we introduce the context-free network (CFN), a siamese-ennead CNN. The CFN takes image tiles as input and explicitly limits the receptive field (or context) of its early processing units to one tile at a time. We show that the CFN includes fewer parameters than AlexNet while preserving the same semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we learn both a feature mapping of object parts as well as their correct spatial arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. Our proposed method for learning visual representations outperforms state of the art methods in several transfer learning benchmarks.
There is also a sizeable literature on solving Jigsaw puzzles computationally (see, for example, @cite_13 @cite_20 @cite_7 ). However, these methods rely on the shape of the tiles or on texture especially in the proximity of the borders of the tiles. These are cues that we avoid when training the Jigsaw puzzle solver, as they do not carry useful information when learning a part detector.
@cite_13 @cite_7 @cite_20
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In the square jigsaw puzzle problem one is required to reconstruct the complete image from a set of non-overlapping, unordered, square puzzle parts. Here we propose a fully automatic solver for this problem, where unlike some previous work, it assumes no clues regarding parts' location and requires no prior knowledge about the original image or its simplified (e.g., lower resolution) versions. To do so, we introduce a greedy solver which combines both informed piece placement and rearrangement of puzzle segments to find the final solution. Among our other contributions are new compatibility metrics which better predict the chances of two given parts to be neighbors, and a novel estimation measure which evaluates the quality of puzzle solutions without the need for ground-truth information. Incorporating these contributions, our approach facilitates solutions that surpass state-of-the-art solvers on puzzles of size larger than ever attempted before. Jigsaw puzzle algorithm is important as it can be applied to many areas such as biology, image editing, archaeology and incomplete crime-scene reconstruction. But, still, some problems exist in the process of practical application, for example, when there are a large number of similar objects in the puzzle fragments, the error rate will reach 30 –50 . When some fragments are missing, most algorithms fail to restore the images accurately. When the number of fragments of the jigsaw puzzle is large, efficiency is reduced. During the intelligent puzzle, mainly the Sum of Squared Distance Scoring (SSD), Mahalanobis Gradient Compatibility (MGC) and other metrics are used to calculate the similarity between the fragments. On the basis of these two measures, we put forward some new methods: 1. MGC is one of the most effective measures, but using MGC to reassemble the puzzle can cause an error image every 30 or 50 times, so we combine the Jaccard and MGC metric measure to compute the similarity between the image fragments, and reassemble the puzzle with a greedy algorithm. This algorithm not only reduces the error rate, but can also maintain a high accuracy in the case of a large number of fragments of similar objects. 2. For the lack of fragmentation and low efficiency, this paper uses a new method of SSD measurement and mark matrix, it is general in the sense that it can handle puzzles of unknown size, with fragments of unknown orientation, and even puzzles with missing fragments. The algorithm does not require any preset conditions and is more practical in real life. Finally, experiments show that the algorithm proposed in this paper improves not only the accuracy but also the efficiency of the operation. 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. This paper describes the development of a procedure that enables a digital computer to solve apictorial'' jigsaw puzzles, i.e., puzzles in which all pieces are uniformly gray and the only available information is the shape of the pieces. The problem was selected because it provided an excellent vehicle to develop computer techniques for manipulation of arbitrary geometric patterns, for pattern identification, and for game solving. The kinds of puzzles and their properties are discussed in detail. Methods are described for characterizing and classifying piece contours, for selecting and ordering pieces that are most likely'' to mate with a given piece, for determining likelihood of fit, for overcoming ambiguities, and for evaluation of the progressive puzzle assembly. An illustration of an actual computer solution of a puzzle is given.