Capturing semantic relationship among images in clusters for efficient content-based image retrieval

Abstract

This paper presents an efficient content-based image retrieval system that captures users' semantic concepts in clusters. These semantically homogeneous clusters aid in the retrieval system to accurately measure the semantic similarity among images and therefore reduce the semantic gap. They also aid in the retrieval system to find matched images in a few candidate clusters and therefore reduce the search space. The extensive experiments demonstrate that the proposed retrieval system outperforms the peer systems to quickly retrieve the desired images in a few iterations.

Fast and Efficient K-means based Algorithm to Content-based Image Clustering

“…But these clustering methods have a problem in the selection of optimum results. The introduced CBIR system in Daves et al [27] retains the semantic concepts of the users in several clusters. The formative semantically clusters assist the retrieval framework to accurately estimate the similarity among dataset images.…”

An efficient semantic – Related image retrieval method

“…In a later approach, the authors [O. Chum et al, 2007;Robert Davis et al, 2012;Norton, D. et al, 2016] represent a new query with multiple points to determine the bounds as in Figure 1(b). This approach uses a clustering method [Charikar, M. et al, 1997] to calculate new query points by using the query results (related images) based on the user's feedback.…”

Görüntülerden Derin Öğrenmeye Dayalı Otomatik Metin Çıkarma: Bir Görüntü Yakalama Sistemi

“…Bir görüntünün metinsel tanımını oluşturma, görüntüdeki nesneleri tanıma, anlamsal ilişkiler, arka plan sahnesini anlama ve bu bilgiyi sözdizimsel olarak doğru cümlelere dönüştürme süreci image capturing çalışma konularıdır. Verilen görüntüyü anlamsal olarak en iyi tanımlayan cümleyi üretmek amaçlanmaktadır [3]. Resim yazısı sorunu hem bilgisayarlı görmenin hem de doğal dil işlemenin bir parçası olarak görülmektedir [4,5].…”