Abstract
In this paper a new image retrieval algorithm is proposed which aims to discard irrelevant images and increase the amount of relevant ones in a large database. This method utilizes a two-stage ant colony algorithm employing in parallel color, texture and spatial information. In the first stage, the synergy of the low-level descriptors is considered to be a group of ants seeking the optimal path to the “food” which is the most similar image to the query, whilst settling pheromone on each of the images that they confront in the high similarity zone. In the second stage additional queries are made by using the highest ranked images as new queries, resulting in an aggregate deposition of pheromone through which the final retrieval is performed. The results prove the system to be satisfactorily efficient as well as fast.
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Konstantinidis, K., Sirakoulis, G.C., Andreadis, I. (2007). An Intelligent Image Retrieval System Based on the Synergy of Color and Artificial Ant Colonies. In: Ersbøll, B.K., Pedersen, K.S. (eds) Image Analysis. SCIA 2007. Lecture Notes in Computer Science, vol 4522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73040-8_88
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DOI: https://doi.org/10.1007/978-3-540-73040-8_88
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