Content-based image retrieval (CBIR) has become an interesting and urgent research topic due to the increase of necessity of indexing and classification of multimedia content in large databases. The low level visual descriptors, such as color-based, texture-based and shape-based descriptors, have been used for the CBIR task. In this paper we propose a color-based descriptor which describes well image contents, integrating both global feature provided by dominant color and local features provided by color correlogram. The performance of the proposed descriptor, called Dominant Color Correlogram descriptor (DCCD), is evaluated comparing with some MPEG-7 visual descriptors and other color-based descriptors reported in the literature, using two image datasets with different size and contents. The performance of the proposed descriptor is assessed using three different metrics commonly used in image retrieval task, which are ARP (Average Retrieval Precision), ARR (Average Retrieval Rate) and ANMRR (Average Normalized Modified Retrieval Rank). Also precision-recall curves are provided to show a better performance of the proposed descriptor compared with other color-based descriptors.
This paper proposes an image steganography scheme, in which a secret image is hidden into a cover image using a secret image sharing (SIS) scheme. Taking advantage of the fault tolerant property of the (k,n)-threshold SIS, where using any k of n shares (k≤n), the secret data can be recovered without any ambiguity, the proposed steganography algorithm becomes resilient to cropping and impulsive noise contamination. Among many SIS schemes proposed until now, Lin and Chan’s scheme is selected as SIS, due to its lossless recovery capability of a large amount of secret data. The proposed scheme is evaluated from several points of view, such as imperceptibility of the stegoimage respect to its original cover image, robustness of hidden data to cropping operation and impulsive noise contamination. The evaluation results show a high quality of the extracted secret image from the stegoimage when it suffered more than 20% cropping or high density noise contamination.