Angelov, Plamen and Sadeghi Tehran, Pouria (2017) Look-a-like : a fast content-based image retrieval approach using a hierarchically nested dynamically evolving image clouds and recursive local data density. International Journal of Intelligent Systems, 32 (1). pp. 82-103. ISSN 0884-8173
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Abstract
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.