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"Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17.99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41.7 second for retrieve session.

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Klasifikasi adalah metode untuk menyusun data secara sistematis menurut aturan-aturan yang telah ditetapkan sebelumnya. Dalam beberapa tahun terakhir metode klasifikasi telah terbukti membantu pekerjaan banyak orang, seperti klasifikasi citra, alat-alat medis, lampu lalu lintas, klasifikasi teks dll. Ada banyak metode yang dapat digunakan untuk memecahkan masalah klasifikasi, metode yang bervariasi ini membuat para peneliti menemukan kesulitan dalam menentukan metode manakah yang terbaik untuk menyelesaikan masalahnya. Artikel ini bertujuan untuk membandingkan kemampuan metode klasifikasi, seperti Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), dan Back-propagation khususnya dalam studi kasus image retrieval (pencarian gambar) dengan lima kategori dataset citra. Hasil penelitian menunjukkan bahwa K-NN memiliki nilai rata-rata akurasi terbaik dengan 82% dan yang tercepat dengan rata-rata waktu komputasi selama 17,99 detik untuk proses pencarian gambar pada semua kategori kelas. Sebaliknya, Backpropagation merupakan metode paling lambat di antara ketiganya. Metode ini rata-rata memerlukan waktu 883 detik untuk sesi pelatihan dan 41,7 detik untuk sesi pencarian gambar."

"Pada laporan ini penulis melakukan perolehan gambar dengan koleksi kain tradisioanal Indonesia. Feature utama gambar yang menjadi sorotan pada tugas akhir ini adalah warna dan tekstur. Upaya meningkatkan keandalan sistem perolehan gambar dilakukan dengan berbagai cara seperti ekspansi kueri, kombinasi dua atau lebih metode ekstraksi feature, pseudo relevance feedback dan user relevance feedback.

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In this work, writer doing an image retrieval on Indonesia tradisional cloth image collection. The main feature of image that became focus on this work is color and texture. Writer try to increase the reliability of image retrieval system using several way, such as query expansion, combination of two of more feature extraction method, pseudo relevance feedback and user relevance feedback."

"This book presents the most recent results and important trends in visual information indexing and retrieval. "

"This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2011, held in Toronto, Canada, in September 2011. The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions. The papers are divided on several topics on medical image retrieval with textual approaches, visual word based approaches, applications and multidimensional retrieval."

"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."

"An efficient Region-Based Image Retrieval (RBIR) system must consider query region determination techniques and target regions in the retrieval process. A query region is a region that must contain a Region of Interest (ROI) or saliency region. A query region determination can be specified manually or automatically. However, manual determination is considered less efficient and tedious for users. The selected query region must determine specific target regions in the image collection to reduce the retrieval time. This study proposes a strategy of query region determination based on the Region Importance Index (RII) value and relative position of the Saliency Region Overlapping Block (SROB) to produce a more efficient RBIR. The entire region is formed by using the mean shift segmentation method. The RII value is calculated based on a percentage of the region area and region distance to the center of the image. Whereas the target regions are determined by considering the relative position of SROB, the performance of the proposed method is tested on a CorelDB dataset. Experimental results show that the proposed method can reduce the Average of Retrieval Time to 0.054 seconds with a 5x5 block size configuration."