Fast Computation of Similarity Based on Jaccard Coefficient for Composition-Based Image Retrieval

Kobayakawa, Michihiro; Kinjo, Shigenao; Hoshi, Mamoru; Ohmori, Tadashi; Yamamoto, Atsushi

doi:10.1007/978-3-642-10467-1_87

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…Bend's shape similarity assessment is the basis of coastline's shape similarity assessment. Considering the coastline's (1) generalization principles, and (2) spatial location features, the Jaccard similarity coefficient [67] of two reorganized coastlines is used to quantize the shape similarity between them. Namely, shape similarity degree between a pair of reorganized coastlines of the same layer C 0 and C 1 , JSC(C 0 , C 1 ), can be calculated by:…”

Section: Dbf Based Shape Similarity Assessment Methodsmentioning

confidence: 99%

Shape Similarity Assessment Method for Coastline Generalization

Zhai

2018

IJGI

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Although shape similarity is one fundamental element in coastline generalization quality, its related research is still inadequate. Consistent with the hierarchical pattern of shape recognition, the Dual-side Bend Forest Shape Representation Model is presented by reorganizing the coastline into bilateral bend forests, which are made of continuous root-bends based on Constrained Delaunay Triangulation and Convex Hull. Subsequently, the shape contribution ratio of each level in the model is expressed by its area distribution in the model. Then, the shape similarity assessment is conducted on the model in a top–down layer by layer pattern. Contrast experiments are conducted among the presented method and the Length Ratio, Hausdorff Distance and Turning Function, showing the improvements of the presented method over the others, including (1) the hierarchical shape representation model can distinguish shape features of different layers on dual-side effectively, which is consistent with shape recognition, (2) its usability and stability among coastlines and scales, and (3) it is sensitive to changes in main shape features caused by coastline generalization.

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Section: Dbf Based Shape Similarity Assessment Methodsmentioning

confidence: 99%

Shape Similarity Assessment Method for Coastline Generalization

Zhai

2018

IJGI

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“…The Jaccard coefficient measures similarity as the intersection divided by the union of the objects. The Jaccard coefficient is mainly used for computing symbol metric or Boolean similarity between individual attributes, because the individual is symbol metric or a Boolean indicator therefore unable to measure the difference of specific value, can only get “is the same as” the results, the Jaccard coefficient is concerned only with the common features among individuals is consistent with this problem [ 26 ]. The formula is as follows:

…”

Section: Methodology and Technologymentioning

confidence: 99%

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs

Chen

Han

Dai

et al. 2015

Computational Intelligence and Neuroscience

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In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.

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“…Further, we used jaccard similarity coefficient [3] for selecting the co-occurring terms with the user query, we called it Co-occurrence Based Query Expansion (CBQE). Some top terms selected from co-occurrence form a term pool of candidate terms.…”

Section: Proposed Approachmentioning

confidence: 99%

Co-occurrence and Semantic Similarity Based Hybrid Approach for Improving Automatic Query Expansion in Information Retrieval

Singh

Sharan

2015

Lecture Notes in Computer Science

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Abstract. Pseudo Relevance feedback (PRF) based query expansion approaches assumes that the top ranked retrieved documents are relevant. But this assumption is not always true; it may also possible that a PRF document may contain different topics, which may or may not be relevant to the query terms even if the documents are judged relevant. In this paper our focus is to capture the limitation of PRF based query expansion and propose a hybrid method to improve the performance of PRF based query expansion by combining corpus based term co-occurrence information and semantic information of term. Firstly, the paper suggest use of corpus based term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, we use semantic similarity approach to rank the query expansion terms obtained from top feedback documents. Third, we combine co-occurrence and semantic similarity together to rank the query expansion terms obtained from first step on the basis of semantic similarity. The experiments were performed on FIRE ad hoc and TREC-3 benchmark datasets of information retrieval. The results show significant improvement in terms of precision, recall and mean average precision (MAP). This experiments shows that the combination of both techniques in an intelligent way gives us goodness of both of them. As this is the first attempt in this direction there is a large scope of improving these techniques.

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Fast Computation of Similarity Based on Jaccard Coefficient for Composition-Based Image Retrieval

Cited by 12 publications

References 3 publications

Shape Similarity Assessment Method for Coastline Generalization

Shape Similarity Assessment Method for Coastline Generalization

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs

Co-occurrence and Semantic Similarity Based Hybrid Approach for Improving Automatic Query Expansion in Information Retrieval

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