SNN tabanlı çok seviyeli eşikleme ile görüntü erişimi

Year 2022, IOCENS’21 Konferansı Ek Sayısı, 98 - 108, 30.09.2022

Mürsel Ozan İncetaş , Mahmut Kılıçaslan , Taymaz Rahkar Farshi

https://doi.org/10.17714/gumusfenbil.1002577

Abstract

Görüntü erişimi, dijital bir görüntü veri tabanından benzer veya özdeş görüntülerin indekslenmesi olarak tanımlanır. Benzer bir dijital görüntü aranırken görüntülerden elde edilen çeşitli öznitelik vektörleri kullanılır. Çünkü görüntülerin pikselleri üzerinde işlem yapmak maliyetli algoritmalar gerektirir. Ayrıca, erişim yaklaşımlarında kullanılan görüntülerin farklı boyutlarda olması olası bir problemdir. Bu nedenle, görüntüleri karşılaştırırken piksel düzeyindeki işlemler yetersiz kalmaktadır. Görüntüleri temsil eden vektörel yapılar gereklilik olarak karşımıza çıkmaktadır. Bu vektörel yapıları elde etme sürecine özellik çıkarımı denir ve içerik tabanlı görüntü erişiminin en önemli aşamalarından biridir. Histogram ise görüntünün boyutlarından bağımsız ve kolaylıkla hesaplanabilen en temel öznitelik vektörüdür. Gri seviyeli görüntülerde histogramın boyutu öznitelik vektörü olarak kullanıma uygundur. Ancak, renkli görüntülerdeki üç farklı kanal, özellik vektörleri olarak kullanılmak için çok fazla veri içerir. Bu nedenle vektör boyutunu küçültmek kaçınılmaz bir işlemdir. Bu çalışmada, insan görsel sisteminden esinlenerek İğnecikli Sinir Ağı modeline dayalı yeni bir çok-seviyeli eşikleme yöntemi önerilmiştir. Önerilen model ile RGB renk kanallarının her biri için 3 ayrı eşik değeri belirlenmiş ve her bir renk kanalı 4 parçaya bölünmüştür. Böylece elde edilen renk paleti ile renk uzayı 64 farklı renge indirgenir. Önerilen yöntem, görüntü erişimi için yaygın olarak kullanılan çok seviyeli eşikleme yöntemleri ile karşılaştırılmıştır. Elde edilen sonuçlar önerilen yöntemin başarısını açıkça göstermektedir.

Keywords

Renk niceleme, İçerik tabanlı görüntü erişimi, Çok seviyeli eşikleme, İğnecikli sinir ağı

Image retrieval with SNN-based multi-level thresholding

Abstract

Image retrieval is defined as indexing similar or identical images in a digital image database. Various feature vectors obtained from the images are used while searching for a similar digital image. However, processing all pixels of the images requires costly algorithms. In addition, it is a possible issue that the images used in retrieval approaches are of different sizes. For this reason, pixel-level operations are insufficient when comparing images. Therefore, it requires vectorial structures that represent images. The process of obtaining these vectorial structures is called feature extraction, and it is one of the most important stages of content-based image retrieval. On the other hand, the histogram is the most basic feature vector that is independent of the dimensions of the image and can be easily calculated. In gray-level images, the size of the histogram is suitable for use as a feature vector. However, three different channels in color images contain too much data to be used as feature vectors. The data of 3 separate histograms are reduced using various thresholding processes and feature vectors are extracted. Therefore, reducing the vector size is an inevitable operation. In this study, a new multi-thresholding method based on the Spiking Neural Network model, inspired by the human visual system, is proposed. With the proposed model, 3 threshold values are determined for each of the RGB color channels, and each color channel is divided into 4 parts. Thus, the color palette of the image is quantized to 64 different colors and a feature vector with 64 elements is obtained. The proposed method was compared with the commonly used multilevel thresholding methods. The results obtained showed that the proposed method is quite successful.

Keywords

Color quantization, Content-based image retrieval, Multilevel thresholding, Spiking neural network

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