Layouts and sub-layouts constitute an important clue while searching a document on the basis of its structure, or when textual content is unknown irrelevant. A sub-layout specifies the arrangement of document entities within a smaller portion of the document. We propose an efficient graph-based matching algorithm, integrated with hash-based indexing, to prune a possibly large search space. A user can specify a combination of sub-layouts of interest using sketch-based queries. The system supports partial matching for unspecified layout entities. We handle cases of segmentation pre-processing errors (for text non-text blocks) with a symmetry maximization-based strategy, and accounting for multiple domain-specific plausible segmentation hypotheses. We show promising results of our system on a database of unstructured entities, containing 4776 newspaper images.
van @cite_5 use layout information for document retrieval. A class of distance measure based on two-step procedure is introduced. In the first step, the distances between the blocks of document and query layouts are calculated. Various types of distance measures like, Manhattan distance of corner points, overlapping area of blocks, difference in width and height, etc., were used to compute distance between every pair of blocks in given layouts. Then, the matching step matches the blocks of query layout to the blocks of reference layout by minimizing the total distance. Since the method uses distance measure, it is not invariant to position and shape of the blocks. Besides, the number of blocks in case of sub-layouts will be lesser and their position and aspect ratios can be different, the total distance between two layouts will be larger and thus, may not match.
@cite_5
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Most methods for document image retrieval rely solely on text information to find similar documents. This paper describes a way to use layout information for document image retrieval instead. A new class of distance measures is introduced for documents with Manhattan layouts, based on a two-step procedure: First, the distances between the blocks of two layouts are calculated. Then, the blocks of one layout are assigned to the blocks of the other layout in a matching step. Different block distances and matching methods are compared and evaluated using the publicly available MARG database. On this dataset, the layout type can be determined successfully in 92.6 of the cases using the best distance measure in a nearest neighbor classifier. The experiments show that the best distance measure for this task is the overlapping area combined with the Manhattan distance of the corner points as block distance together with the minimum weight edge cover matching. Given a set S of n sites (points), and a distance measure d , the nearestneighborsearching problem is to build a data structure so that given a query point q , the site nearest to q can be found quickly. This paper gives data structures for this problem when the sites and queries are in a metric space. One data structure, D(S) , uses a divide-and-conquer recursion. The other data structure, M(S,Q) , is somewhat like a skiplist. Both are simple and implementable. The data structures are analyzed when the metric space obeys a certain sphere-packing bound, and when the sites and query points are random and have distributions with an exchangeability property. This property implies, for example, that query point q is a random element of ( S q ) . Under these conditions, the preprocessing and space bounds for the algorithms are close to linear in n . They depend also on the sphere-packing bound, and on the logarithm of the distanceratio ( (S) ) of S , the ratio of the distance between the farthest pair of points in S to the distance between the closest pair. The data structure M(S,Q) requires as input data an additional set Q , taken to be representative of the query points. The resource bounds of M(S,Q) have a dependence on the distance ratio of S ( )Q . While M(S,Q) can return wrong answers, its failure probability can be bounded, and is decreasing in a parameter K . Here K≤ |Q| n is chosen when building M(S,Q) . The expected query time for M(S,Q) is O(Klog n)log ( (S Q) ) , and the resource bounds increase linearly in K . The data structure D(S) has expected O( log n) O(1) query time, for fixed distance ratio. The preprocessing algorithm for M(S,Q) can be used to solve the all nearest neighbor problem for S in O(n(log n) 2 (log ϒ(S)) 2 ) expected time. In most existing retrieval models, documents are scored primarily based on various kinds of term statistics such as within-document frequencies, inverse document frequencies, and document lengths. Intuitively, the proximity of matched query terms in a document can also be exploited to promote scores of documents in which the matched query terms are close to each other. Such a proximity heuristic, however, has been largely under-explored in the literature; it is unclear how we can model proximity and incorporate a proximity measure into an existing retrieval model. In this paper,we systematically explore the query term proximity heuristic. Specifically, we propose and study the effectiveness of five different proximity measures, each modeling proximity from a different perspective. We then design two heuristic constraints and use them to guide us in incorporating the proposed proximity measures into an existing retrieval model. Experiments on five standard TREC test collections show that one of the proposed proximity measures is indeed highly correlated with document relevance, and by incorporating it into the KL-divergence language model and the Okapi BM25 model, we can significantly improve retrieval performance. Recently, a new document metric called the word mover's distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely un-supervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric. The supervised training minimizes the stochastic leave-one-out nearest neighbor classification error on a per-document level by updating an affine transformation of the underlying word embedding space and a word-imporance weight vector. As the gradient of the original WMD distance would result in an inefficient nested optimization problem, we provide an arbitrarily close approximation that results in a practical and efficient update rule. We evaluate S-WMD on eight real-world text classification tasks on which it consistently outperforms almost all of our 26 competitive baselines.