A fast and effective image retrieval scheme using color-, texture-, and shape-based histograms

The rapid growth of digital image collections has prompted the need for development of software tools that facilitate efficient searching and retrieval of images from large image databases. Towards this goal, we propose a content-based image retrieval scheme for retrieval of images via their color, texture, and shape features. Using three specialized histograms (i.e. color, wavelet, and edge histograms), we show that a more accurate representation of the underlying distribution of the image features improves the retrieval quality. Furthermore, in an attempt to better represent the user’s information needs, our system provides an interactive search mechanism through the user interface. Users searching through the database can select the visual features and adjust the associated weights according to the aspects they wish to emphasize. The proposed histogram-based scheme has been thoroughly evaluated using two general-purpose image datasets consisting of 1000 and 3000 images, respectively. Experimental results show that this scheme not only improves the effectiveness of the CBIR system, but also improves the efficiency of the overall process.

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