Sketch-based image retrieval often needs to optimize the trade-off between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of sketch-based image retrieval.

Social media sharing websites like Flickr allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval and organization. Tag-based image search is an important method to find images contributed by social users in such social websites. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a social re-ranking system for tag-based image retrieval with the consideration of image’s relevance and diversity. We aim at re-ranking images according to their visual information, semantic information and social clues. The initial results include images contributed by different social users. Usually each user contributes several images. First we sort these images by inter-user re-ranking. Users that have higher contribution to the given query rank higher. Then we sequentially implement intra-user re-ranking on the ranked user’s image set, and only the most relevant image from each user’s image set is selected. These selected images compose the final retrieved results. We build an inverted index structure for the social image dataset to accelerate the searching process. Experimental results on Flickr dataset show that our social re-ranking method is effective and efficient.

Recently, a new probability model dubbed the Laplacian transparent composite model (LPTCM) was developed for DCT coefficients, which could identify outlier coefficients in addition to providing superior modeling accuracy. In this paper, we aim at exploring its applications to image compression. To this end, we propose an efficient nonpredictive image compression system, where quantization (including both hard-decision quantization (HDQ) and soft-decision quantization (SDQ)) and entropy coding are completely redesigned based on the LPTCM. When tested over standard test images, the proposed system achieves overall coding results that are among the best and similar to those of H.264 or HEVC intra (predictive) coding, in terms of rate versus visual quality. On the other hand, in terms of rate versus objective quality, it significantly outperforms baseline JPEG by more than 4.3 dB in PSNR on average, with a moderate increase on complexity, and ECEB, the state-of-the-art nonpredictive image coding, by 0.75 dB when SDQ is OFF (i.e., HDQ case), with the same level of computational complexity, and by 1 dB when SDQ is ON, at the cost of slight increase in complexity. In comparison with H.264 intracoding, our system provides an overall 0.4-dB gain or so, with dramatically reduced computational complexity; in comparison with HEVC intracoding, it offers comparable coding performance in the high-rate region or for complicated images, but with only less than 5% of the HEVC intracoding complexity. In addition, our proposed system also offers multiresolution capability, which, together with its comparatively high coding efficiency and low complexity, makes it a good alternative for real-time image processing applications.

The use of local prediction in difference expansion reversible watermarking provides very good results, but at the cost of computing for each pixel a least square predictor in a square block centered on the pixel. This correspondence investigates the reduction of the mathematical complexity by computing distinct predictors not for pixels, but for groups of pixels. The same predictors are recovered at detection. Experimental results for the case of prediction on the rhombus defined by the four horizontal and vertical neighbors are provided. It is shown that by computing a predictor for a pair of pixels, the computational cost is halved without any loss in performance. A small loss appears for groups of three and four pixels with the advantage of reducing the mathematical complexity to a third and a fourth, respectively.

In this paper, we present a Image search re-ranking is an effective approach to refine the text-based image search result. Most existing re-ranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search re-ranking. Based on the classifiers for all the pre-defined attributes, each image is represented by a attribute feature consisting of the responses from these classifiers. A hyper-graph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hyper-graph ranking is performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. We conduct experiments on 300 queries in MSRA-MM V2.0 dataset. The experimental results demonstrate the effectiveness of our approach.

In this paper, we present a statistical analysis of Scalable image search based on visual similarity has been an active topic of research in recent years. This paper first model retrieval ranks as graphs of candidate images and propose a graph-based query specific fusion approach, where multiple graphs are merged and re-ranked by conducting a link analysis on a fused graph. Next it proposed a query-adaptive image search system. Query adaptive weights are computed between query image and data base images. Based on the weights the images are ranked. The sorted ranked images are retrieved for user.