Yadong Wu

Local patterns constrained image histograms for image retrieval

In this paper, we present local patterns constrained image histograms (LPCIH) for efficient image retrieval. Extracting information through combining local texture patterns with global image histogram, LPCIH is an effective image feature representation method with a flexible image segmentation process. This kind of feature representation is robust and invariant for several image transforms, such as rotation, scaling and damaging. LPCIH method is efficient for several difficult image retrieval tasks, such as rotated and damaged gray image retrieval. Because many traditional image retrieval methods are not suitable for the difficult gray image retrieval tasks, LPCIH is valuable for many real-world applications of image retrieval. Experimental results show that the LPCIH method is consistently efficient, effective and it offers advantages over the state-of-the-art image retrieval methods.

Document Image Retrieval with Local Feature Sequences

In recent years, many document image retrieval algorithms have been proposed. However, most of the current approaches either need good quality images or depend on the page layout structure. This paper presents a fast, accurate and OCR-free image retrieval algorithm using local feature sequences which can describe the intrinsic, unique and page-layout-free characteristics of document images. With a simple preprocessing step, the local feature sequences can be extracted without print-core detection and image registration. Then an efficient coarse-to-fine common substring matching strategy is applied to do local feature sequences matching. Beyond a single matching score, this approach can locate the matched parts word by word. It well handles the challenges including low resolution, different language, rotation and incompleteness and N-up. The encouraging experiment results on a large scale document image database show the retrieval outputs are sufficient good to be used directly as document image identification results.

Maximum normalized spacing for efficient visual clustering

In this paper, for efficient clustering of visual image data that have arbitrary mixture distributions, we propose a simple distance metric learning method called Maximum Normalized Spacing (MNS) which is a generalized principle based on Maximum Spacing [12] and Minimum Spanning Tree (MST). The proposed Normalized Spacing (NS) can be viewed as a kind of adaptive distance metric for contextual dissimilarity measure which takes into account the local distribution of the data vectors. Image clustering is a difficult task because there are multiple nonlinear manifolds embedded in the data space. Many of the existing clustering methods often fail to learn the whole structure of the multiple manifolds and they are usually not very effective. Combining both the internal and external statistics of clusters to capture the density structure of manifolds, MNS is capable of efficient and effective solving the clustering problem for the complex multi-manifold datasets in arbitrary metric spaces. We apply this MNS method into the practical problem of multi-view image clustering and obtain good results which are helpful for image browsing systems. Using the COIL-20 [19] and COIL-100 [18] multi-view image databases, our experimental results demonstrate the effectiveness of the proposed MNS clustering method and this clustering method is more efficient than the traditional clustering methods.