An efficient indexing structure and image representation for content-based image retrieval

In this paper, we present the following schemes for a content-based image search: (1) A fast image search algorithm that can significantly reduce similarity calculation compared to a full comparison of every database image. (2) A compact image representation scheme that can describe the global/local information of the images and provide successful retrieval performance. For fast searches, a tree is constructed by successfully dividing nodes into the desired depth level by working from the root to the leaf nodes using the k-means algorithm. When the query is completed, we traverse the tree top-down by minimizing the route taken between the query image and node centroid until we meet the undivided nodes. Within undivided nodes, the algorithm of triangle inequality is used to find the images most similar to the query. For compact image representation, RGB color histogram features which are quantized into 16 bins each of the R, G, and B channels are used for global information. Dominant hue, saturation, and value which are extracted from the HSV joint histogram in the localized regions within the image are used for local information. These features are sufficiently compact to index image features in large database systems. For experiments on the retrieval efficiency, the use of the proposed method provided substantial performance benefits by reducing the image similarity calculation up to an average of a 96% and for experiments on the retrieval effectiveness, in the best case, it provide a 36.8% recall rate for a whale query image and a 100% precision rate for an eagle query image. The overall performance was a 20.0% recall rate and a 72.5% precision rate.