Implementasi Content Based Video retrieval Menggunakan Speede-Up Robust Features (Surf)

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ABSTRAK

Temu balik video ataupun temu balik video berdasarkan konten gambar adalah salah
satu bidang penelitian dari sistem temu balik informasi. Content Based Video
Retrieval memiliki empat tahapan yaitu segmentasi video yang menghasilkan frame –
frame, ekstraksi keyframe merupakan tahapan memilih frame kunci dari semua frame
koleksi video, tahapan ekstraksi fitur menggunakan algoritma Speeded-Up Robust
Features (SURF). Surf adalah detektor dan deskriptor fitur citra yang cepat. Dan kini
banyak digunakan secara luas dalam aplikasi kompter visual seperti dalam aplikasi
temu balik video.Surf pertama kali dipresentasikan oleh Herbert Bay pada konferensi
Eropa 2006 tentang Computer Vision dan sebagian terinspirasi oleh Scale-Invariant
Features Transform (SIFT). Titik interest atau key point dari citra query dan keyframe
direpresentasikan oleh vektor yang kemudian akan dibandingkan dalam tahap
pencocokan fitur. Tahap pencocokan fitur dilakukan dengan membandingkan nilai
fitur pada queryyang diberikan dengan keyframe pada koleksi video setiap kategori.
Parameter yang digunakan untuk mengukur kualitas dari hasiltemu balik video adalah
nilai dari Recall, Precision, dan Running Time. Berdasarkan hasil pengujian
menggunakan 2 jenis query, Query Bukan Frame yaitu query yang diambil diluar
frame koleksi video dan Query Dari Frame yang diambil dari frame koleksi video.rata

rata nilai Recall54,75%, nilai rata – rata Precision 37,5%, serta nilai Running Time
73,56 detik. Query Dari Frame dengan nilai rata – rata Recall 51%, nilai rata – rata
Precision 59%, dan Running Time 121,67 detik.
Kata Kunci : Video, Citra, Temu Balik, Ekstraksi Fitur, Content Based Video
Retrieval (CBVR), Speeded-Up Robust Features (SURF).

Universitas Sumatera Utara

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IMPLEMENTATION OF CONTENT BASED VIDEO RETRIEVAL WITH
SPEEDED-UP ROBUST FEATURES (SURF)

ABSTRACT

Image Retrieval (IR) or Content-Based Image Retrieval (CBIR) is a well-known field
of research in Information Retrieval System (IRS). Content Based Video Retrieval has
four stages: video segmentation that produces frames, keyframe extraction is the stage
of selecting key frames of all video frames, feature extraction stages using SpeededUp Robust Features (SURF) algorithm. Surf is a fast image detector and descriptor
feature. It is now widely used in visual compiler applications such as in video.Surf

backlash apps were first presented by Herbert Bay at the 2006 European conference
on Computer Vision and partly inspired by the Scale-Invariant Features Transform
(SIFT). The interest point or key point of the query image and keyframe is represented
by the vector which will then be compared in the feature matching stage. The feature
matching stage is performed by comparing the feature value of the given query with
the keyframe on the video collection of each category. The parameters used to
measure the quality of video feedbacks are the values of Recall, Precision, and
Running Time. Based on the test results using two types of queries, Query Not Frame
is a query that is taken outside the frame of the video collection and Query From
Frame taken from frame collection video.Average Recall value 54.75%, Precision
average 37.5%, and Running Time value 73.56 seconds. Query From Frame with
Recall 51% average value, Precision 59% average value, and Running Time 121.67
sec.
Keywords : Video, Image, Retrieval, Feature Extraction, Content Based Video
Retrieval (CBVR), Speeded-Up Robust Features (SURF).

Universitas Sumatera Utara