Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are widely used in various computer applications. They emphasize different aspects of image contents. In this letter, we propose to combine them in sparse coding for categorizing scene images. First, we regularly extract LBP and SIFT features from training images. Then, corresponding to each feature, a visual word codebook is constructed. The obtained LBP and SIFT codebooks are used to create a two-dimensional table, in which each entry corresponds to an LBP visual word and a SIFT visual word. Given an input image, LBP and SIFT features extracted from the same positions of this image are encoded together based on sparse coding. After that, spatial max pooling is adopted to determine the image representation. Obtained image representations are converted into one-dimensional features and classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets of Scene Categories 8 and MIT 67 Indoor Scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner is effective for scene categorization.

The paper proposes a constant bit rate (CBR) control algorithm for motion JPEG2000 (MJ2). In MJ2 coding, every frame can be coded at similar target bitrate due to the accurate rate control feature. Moreover, frames of the same scene have the similar rate-distortion (RD) characters. The proposed method estimates the initial cutoff threshold of the current frame according to the previous frame's RD information. This iterative method reduces computational cost significantly. As opposed to previous algorithms, it can be used at any compression ratio. Experiments show that the performance is comparable to normal JPEG2000 coding.

The paper proposes an algorithm to expose spliced photographs. Firstly, a graph-based segmentation, which defines a predictor to measure boundary evidence between two neighbor regions, is used to make greedy decision. Then the algorithm gets prediction error image using non-negative linear least-square prediction. For each pair of segmented neighbor regions, the proposed algorithm gathers their statistic features and calculates features of gray level co-occurrence matrix. K-means clustering is applied to create a dictionary, and the vector quantization histogram is taken as the result vector with fixed length. For a tampered image, its noise satisfies Gaussian distribution with zero mean. The proposed method checks the similarity between noise distribution and a zero-mean Gaussian distribution, and follows with the local flatness and texture measurement. Finally, all features are fed to a support vector machine classifier. The algorithm has low computational cost. Experiments show its effectiveness in exposing forgery.

This paper proposes two efficient rate control algorithms for Motion JPEG2000. Both methods provide accurate visual quality control under buffer constraints. Frames of the same scene usually have the similar rate-distortion (R-D) characters. The proposed methods predict the R-D models of uncoded frames forwardly or bilaterally according to those of coded frames. Experimental results demonstrate that the proposed algorithms offer visual quality improvements over similar competing methods and save a large amount of memory simultaneously.