A Comparative Study of Content Based Image Retrieval Trends and Approaches

Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed. .

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