Processing images for specific targets on a large scale has to handle various kinds of contents with regular processing steps. To segment objects in one image, we utilized dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) to build a background (BG) gray-level variation mesh, which can help to identify BG and object regions. It was developed from a macroscopic perspective on image BG gray levels and implemented using standard procedures, thus robustly dealing with large-scale database images. The image segmentation capability of existing methods can be exploited by the BG mesh to improve object segmentation accuracy. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Content-based image retrieval (CBIR) was carried out to evaluate the object segmentation capability in dealing with large-scale database images. Retrieval precision-recall (PR) and rank performances, with and without SEGON, were compared. For multi-instance retrieval with shape feature, AdaBoost was used to select salient common feature elements. For color features, the histogram intersection between two scalable HSV descriptors was calculated, and the mean feature vector was used for multi-instance retrieval. The distance measure for color feature can be adapted when both positive and negative queries are provided. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance with multifeature query. Experiments showed that the proposed object segmentation method outperforms others by 21% in the PRI. Performing SEGON-enabled CBIR on large-scale databases also improves on the PR performance reported elsewhere by up to 42% at a recall rate of 0.5. The proposed object segmentation method can be extended to extract other image features, and new feature types can be incorporated into the algorithm to further improve the image retrieval performance.

Performing Content-Based Image Retrieval (CBIR) on Internet connected databases through Peer-to-Peer (P2P) network (P2P-CBIR) effectively explores the large-scale image database distributed over connected peers. In additional to enlarge the retrieval scale from server-client to P2P networks, the required computation and network traffics for performing CBIR can also be distributed. Decentralized unstructured P2P framework is adopted to perform the P2P-CBIR in considering control flexibility, under which reconfiguration is feasible to improve the retrieval performance. Based on this framework, the proposed P2P-CBIR can progressively refine the accuracy of retrieved images when they are transmitted on the routes toward the query peer. With flexible P2P framework, reconfiguration can be carried out on the P2P-CBIR, which helps to improve retrieval efficiency. Experiments verified that the P2P-CBIR with reconfiguration outperforms the one without, and the latter outperforms server-client approaches in both retrieval accuracy and network traffics.

Multiple description coding (MDC) provides stable wireless multimedia communications with the help of multiple transmission paths, while distributed video coding (DVC) effectively shifts the encoder complexity to decoder for mobile media devices. We proposed to combine MDC with DVC, abbreviated as MDVC, to construct a reliable and efficient video codec system. To improve the codec performance, the DPCM prediction gain is exploited from the correlations among sub-video sequences inside the MDVC to increase the reconstructed video quality. In addition, a MDVC central decoder control method, operated dynamically at frame-level, is proposed to improve the adaptation capability, as compared to the current MDC central decoder that selects one best description for the output video. The intra-and inter-correlations among MDC descriptions were investigated to make this frame selection decision for the central decoder. Simulations showed that the proposed MDVC control method demonstrates higher average PSNR and lower PSNR variations of the output videos, with the help of DPCM prediction gain and the efficient central decoder control method that correctly selected high quality WZ frames for low quality key-frames.