Scaling Up Ensemble of Adaptations for Classification by Approximate Nearest Neighbor Retrieval

2017 ◽  
pp. 154-169 ◽  
Author(s):  
Vahid Jalali ◽  
David Leake
Keyword(s):  
Nearest Neighbor ◽  
Scaling Up ◽  
Approximate Nearest Neighbor

scholarly journals Pengenalan Motif Karawo Menggunakan Ekstraksi Fitur SIFT Dan Approximate Nearest Neighbor

2019 ◽  
Author(s):  
Syahrial
Keyword(s):  
Nearest Neighbor ◽  
Feature Matching ◽  
Recognition Accuracy ◽  
Sift Algorithm ◽  
Approximate Nearest Neighbor ◽  
Single Pattern ◽  
Tree Data ◽  
Threshold Testing ◽  
Root Word ◽  
Tree Data Structure

An art culture from Gorontalo became iconic handcraft is kerawang or karawo. The word “karawo” came from root word of “mokarawo” which means slicing or making holes. It’s created with precision, carefulness, and patience in work using handmade masterpiece. Pattern of karawo itself held four kinds which is flora, fauna, geometric, and nature. From those kinds born vary pattern which come difficult to identify both its names and its kind. Karawo patterns can be form as a single pattern or a pattern that it parts came from several or many pattern combined. Those patterns had its own characteristic from shape and scale perspective. Identifying single pattern on combined pattern are particularly a problem because it’s combined involve scaling and rotation. This research is recognizing single pattern on combined pattern using feature extraction SIFT algorithm which is capable extract feature that invariant from scale and rotation. Feature matching using approximate nearest neighbor (aNN) for similarity of features labor best bin first strategy on kd-tree data structure. Those methods can be a reference to recognize single pattern on combined pattern using from range 5 to 20 match features as a threshold. Testing result indicated recognition accuracy is good which range form 76.36% to 85.45% on recognize the kind of karawo pattern and 76.36% on its name.

scholarly journals Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor

2017 ◽  
Vol 14 (3) ◽  
pp. 651-661 ◽  
Author(s):  
Baghdad Science Journal
Keyword(s):  
Nearest Neighbor ◽  
Daily Basis ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Suggested Approach ◽  
Approximate Nearest Neighbor ◽  
Invariant Feature ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.

Fusion of multiple approximate nearest neighbor classifiers for fast and efficient classification

2004 ◽  
Vol 5 (4) ◽  
pp. 239-250 ◽  
Author(s):  
P. Viswanath ◽  
M. Narasimha Murty ◽  
Shalabh Bhatnagar
Keyword(s):  
Nearest Neighbor ◽  
Approximate Nearest Neighbor ◽  
Nearest Neighbor Classifiers
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