In existing image retrieval systems using a multipoint query, the neighborhood of the various optimal query points is determined in the same way. The different nature of the various optimal query points was not taken into consideration. This approach confines the performance of the system. To overcome this confinement, in this paper, we propose an image retrieval method through adaptive weights (Aweight) which is possible to compute the various optimal query points, optimal weights and improved distance functions to improve accuracy. In addition, our method constructs clusters without re-clustering the whole feedback image set. The experiments were performed on a set of 10,800 images and the results demonstrate that the proposed method improves performance of system in terms of accuracy.

Many previous techniques were designed to retrieve semantic images in a particular neighborhood of the image query and thus bypassing the semantically related images in the whole feature space. Several methods were originally designed to retrieve semantically images in the same space feature but with low precision. In this paper, we propose a semantic - Related Image Retrieval method (SRIR), which can retrieve semantic images of the same space in high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.

In this paper, we present a new technique to compare image similarity, called HISG (Histogram Graph), that utilizes a weighted undirected graph for each color, and its each vertex is a bin of histogram. As a result, the technique is able to take advantage of color location, and smaller space overhead but is not sensitive to rotation and translation. We carried out an experiment on an image database containing 12,960 landscape images. Experimental result shows that our technique is more effective than the local color histogram and cell/color histograms.