Although many systems based on global or local descriptors have shown promising results for logo recognition, they have handled all logos with the same structure and not considered their diversities. Therefore, with the logo scale increasing, the general way cannot recognize each logo perfectly. To overcome this limitation, we propose a novel strategy to match query and each logo individually using these features. First, a new conception named logo density is introduced as important semantic information for logos. Second, matching density is given according to the logo density and by utilizing it in logistic function an individualized matching strategy is developed to obtain accurate similarity for query and a logo. Finally, we present a fast recognition algorithm based upon bag-of-words model to realize scalable logo recognition. Our method is evaluated on two challenging datasets (our 10,000-class logo dataset and FlickrLogos-27). Experiments demonstrate its superior performance comparing to previous methods.

This paper introduces a novel pre-locating algorithm for rapid logo detection in unconstrained color images. This work is distinguished by two major contributions. The first is a new method of representation for logo called “spatial connected component descriptor” (SCCD) containing connected component (CC) prediction model and effective-CC pixel distribution histogram. The former represents combinations between CCs based on color and spatial relationships of CCs. While the latter describes the pixel distribution information of effective CCs. The two parts capture the layout of logos from different points. The second is a logo pre-locating algorithm by the means of SCCD to search for logo prediction regions in test images, on which some content-based features are used for logo matching. Experimental results illustrate that our pre-locating algorithm speeds up logo detection to a great extent and shows precise location compared to previous systems.

The italic detection and slant rectification is a key step of optical character recognition (OCR). In this paper, a novel method is proposed to detect and rectify italic characters in Chinese advertising images. Based on observations on structures of many characters, the centroid angle is proposed and a statistical study on it is presented. According to the statistical results, the centroid angle of a Chinese character approximately obeys a Gaussian distribution with its slant angle. Moreover, a Markov Random Field (MRF) model, considering the font-face similarity of neighboring characters and the strong correlation between the centroid angle and the slant angle of a character, is then presented to estimate the slant angle of a character. The italic characters can be detected and rectified by the estimated angle. The experimental results demonstrate the proposed method is effective and applicable.

In this paper, a novel Chinese character localization method is proposed for texts in advertising images. To deal with the texts with gradient color, a color clustering method based on edge is introduced to separate the color image into homogeneous color layers. To solve the problem of locating characters varied in size, style and arranged in irregular direction, a novel character localization method is proposed, which integrates structure and CC-clustering to locate characters according to reliable features of characters. Finally, a new noise removal method based on stroke width histogram is employed to remove all non-characters connected components, and then all characters are located. The experimental results show that the proposed method can effectively locate characters in advertising images.

We present a novel approach to measuring similarity between objects based on matching local “appearance contextual descriptor”. The descriptor has two components: Histogram of Oriented Gradient feature representing local patch appearance and the contextual descriptor capturing not only the spatial distribution of the non-reference patches relative to the reference patch but also the appearance similarities between the reference patch and the non-reference patches in the region. Corresponding patches within two similar objects will have similar contextual descriptors, though the patch appearances may have some difference. We treat recognition in a nearest-neighbor classification framework and match object in regions with no prior learning. We compare our method to commonly used methods and demonstrate its applicability to object detection and recognition.

We consider the image classification problem based on the similarities between images. The choice of the similarity is related to the particular applications, and it could be based on color, texture, bag-of-features, or even more complex kernels. As long as the pair-wise similarity matrix is transformed into a positive semidefinite one, the similarities of images could be treated as kernels. This transformation makes it possible for kernel methods to solve the similarity-based image classification problem. In this paper, we propose a novel kernelized classification framework based on sparse representation. This new framework casts the classification as finding a sparse linear representation of test image with respect to training images. Unlike the former works, we do this sparse coding procedure through a proposed kernelized orthogonal matching pursuit algorithm, which is performed in inner product space rather than Euclidean space. Through a proper choice of the similarity function, the proposed approach can be applied to diverse image classification problems. Comparative experiments between the proposed method and other existing methods, on two real datasets (Caltech-101 and Face Rec) show that our method performed better.

A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters. By this means, each music signal is decomposed into a bag of ldquoaudio wordsrdquo. Subsequently, the pLSA model of each musical genre is trained through a new iterative training procedure and well-known EM algorithm. This training procedure can iteratively update the pLSA model parameters by discriminatively computing weight of each training music signal and evidently improve the model's discriminative performance. Finally, these models can be used to classify new unseen music signals. Experiments on two commonly utilized databases show that our pLSA based approach can give promising results and the iterative learning procedure is effective.