State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. A search is performed by comparing query object's feature vector with those for database images. However, a database image vector generally carries mixed information of an entire image which may contain multiple objects and background. Search quality is degraded by such noisy (or diluted) feature vectors. We address this issue by introducing the concept of pseudo-objects to approximate candidate objects in database images. A pseudo-object is a subset of proximate feature points in an image with its own feature vector to represent a local area. We investigate effective methods (e.g., Grid, G-means, and GMM-BIC) to estimate pseudo-objects. Experimenting over two consumer photo benchmarks, we demonstrate the proposed methods significantly outperforming other state-of-the-art object retrieval algorithms.

In light of the strong demands for semantic search over large-scale consumer photos, which generally lack reliable user-provided annotations, we investigate the feasibility and challenges entailed by the new paradigm, concept search - retrieving visual objects by large-scale automatic concept detectors with keywords. We investigate the problem in three folds: (1) the effective concept mapping and selection methods over large-scale concept ontology; (2) the quality and feasibility of the pre-trained concept detectors applying on cross-domain consumer data (i.e., Flickr photos); (3) the search quality by fusing automatic concepts and user-annotated data (tags). Through experiments over large-scale benchmarks, TRECVID and Flickr550, we confirm the effectiveness of concept search in the proposed framework, where the semantic mapping by web-based kernel function over Google snippets significantly outperforms conventional WordNet-like methods both in accuracy and efficiency.

The objective of this work is to reconstruct high quality gray-level images from bilevel halftone images. We develop optimal inverse halftoning methods for several commonly used halftone techniques, which include dispersed-dot ordered dither, clustered-dot ordered dither, and error diffusion. At first, the least-mean-square (LMS) adaptive filtering algorithm is applied in the training of inverse halftone filters. The resultant optimal mask shapes are significantly different for various halftone techniques, and these mask shapes are also quite different from the square shape that was frequently used in the literature. In the next step, we further reduce the computational complexity by using lookup tables designed by the minimum mean square error (MMSE) method. The optimal masks obtained from the LMS method are used as the default filter masks. Finally, we propose the hybrid LMS-MMSE inverse halftone algorithm. It normally uses the MMSE table lookup method for its fast speed. When an empty cell is referred, the LMS method is used to reconstruct the gray-level value. Consequently, the hybrid method has the advantages of both excellent reconstructed quality and fast speed. In the experiments, the error diffusion yields the best reconstruction quality among all three halftone techniques.

A precise error tracking scheme for robust transmission of real-time H.263 video is presented. By utilizing a feedback channel, the decoder reports the addresses of corrupted blocks induced by transmission errors back to the encoder. With these negative acknowledgments, the encoder can precisely calculate and track the propagated errors by examining the backward motion dependency for each pixel in the current encoding frame. With this precise tracking, the error-propagation effects can be terminated completely by INTRA refreshing the affected macroblocks. In addition, by utilizing the four-corner tracking approximation and the linear motion model, a fast algorithm is also developed to further reduce the computation and memory requirements. The simulations show that both schemes yield significant video quality improvements in error-prone environments. The advantages of the low memory requirement and the low computation complexity are particularly suitable for real time implementation.