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This paper presents a new approach to derive the image feature descriptor from the dot-diffused block truncation coding (DDBTC) compressed data stream. The image feature descriptor is simply constructed from two DDBTC representative color quantizers and its corresponding bitmap image. The color histogram feature (CHF) derived from two color quantizers represents the color distribution and image contrast, while the bit pattern feature (BPF) constructed from the bitmap image characterizes the image edges and textural information. The similarity between two images can be easily measured from their CHF and BPF values using a specific distance metric computation. Experimental results demonstrate the superiority of the proposed feature descriptor compared to the former existing schemes in image retrieval task under natural and textural images. The DDBTC method compresses an image efficiently, and at the same time, its corresponding compressed data stream can provide an effective feature descriptor for performing image retrieval and classification. Consequently, the proposed scheme can be considered as an effective candidate for real-time image retrieval applications.Index Terms-Dot-diffused block truncation coding (DDBTC), feature descriptor, image classification, image retrieval.

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This paper reviews the former existing scheme on (n, n)-multiple secret sharing (MSS) for color images along with its slight limitation. This scheme generates a set of n shared images from a set of n secret images using the Chinese remainder theorem (CRT) and Boolean exclusive-OR (XOR) operation. This scheme works well if the number of secret images n is even number. However, the former scheme has a slight problem while the number of secret images n is an odd number. This paper proposes a new technique to overcome this problem by introducing symmetric and transferred masking coefficients to generate a set of shared images. To further improve the security level of the proposed method, a set of secret images is first transformed with hyperchaotic scrambling method before generating shared images. The security of the proposed (n, n)-MSS can also be increased by merging a shared color image into 2-D matrix representation. As documented in the experimental results, the proposed method offers a promising result on (n, n)-MSS scheme regardless of the number of secret images n is odd or even number. In addition, the proposed method outperforms the former existing (n, n)-MSS schemes in terms of quantitative measurements. INDEX TERMS Chinese remainder theorem, exclusive OR, secret sharing, symmetric masking coefficient, transferred masking.