TY - JOUR

T1 - Look-a-like

T2 - a fast content-based image retrieval approach using a hierarchically nested dynamically evolving image clouds and recursive local data density

AU - Angelov, Plamen

AU - Sadeghi Tehran, Pouria

N1 - This is the peer reviewed version of the following article: Angelov, P. and Sadeghi-Tehran, P. (2016), Look-a-Like: A Fast Content-Based Image Retrieval Approach Using a Hierarchically Nested Dynamically Evolving Image Clouds and Recursive Local Data Density. Int. J. Intell. Syst.. doi: 10.1002/int.21837 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/int.21837/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2017/1

Y1 - 2017/1

N2 - The need to find related images from big data streams is shared by many professionals, such as architects, engineers, designers, journalist, and ordinary people. Users need to quickly find the relevant images from data streams generated from a variety of domains. The challenges in image retrieval are widely recognised and the research aiming to address them led to the area of CBIR becoming a 'hot' area. In this paper, we propose a novel computationally efficient approach which provides a high visual quality result based on the use of local recursive density estimation (RDE) between a given query image of interest and data clouds/clusters which have hierarchical dynamically nested evolving structure. The proposed approach makes use of a combination of multiple features. The results on a data set of 65,000 images organised in two layers of an hierarchy demonstrate its computational efficiency. Moreover, the proposed Look-a-like approach is self-evolving and updating adding new images by crawling and from the queries made.

AB - The need to find related images from big data streams is shared by many professionals, such as architects, engineers, designers, journalist, and ordinary people. Users need to quickly find the relevant images from data streams generated from a variety of domains. The challenges in image retrieval are widely recognised and the research aiming to address them led to the area of CBIR becoming a 'hot' area. In this paper, we propose a novel computationally efficient approach which provides a high visual quality result based on the use of local recursive density estimation (RDE) between a given query image of interest and data clouds/clusters which have hierarchical dynamically nested evolving structure. The proposed approach makes use of a combination of multiple features. The results on a data set of 65,000 images organised in two layers of an hierarchy demonstrate its computational efficiency. Moreover, the proposed Look-a-like approach is self-evolving and updating adding new images by crawling and from the queries made.

KW - recursive density estimation (RDE)

KW - dynam- ically evolving hierarchy of data clouds

KW - content-based image retrieval (CBIR)

U2 - 10.1002/int.21837

DO - 10.1002/int.21837

M3 - Journal article

VL - 32

SP - 82

EP - 103

JO - International Journal of Intelligent Systems

JF - International Journal of Intelligent Systems

SN - 0884-8173

IS - 1

ER -