Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

A NEW DESCRIPTOR BASED ON 2D DCT FOR IMAGE RETRIEVAL

International audienceContent-based image retrieval relies on feature comparison between images. So the selection of feature vector is important. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. We propose a new feature vector in DCT domain. Our method selects part of DCT coefficients inside each block to construct AC-Pattern and use DC coefficients between neighboring blocks to construct DC-Pattern. Two histograms are formed and parts of them are used to build a descriptor vector integrating features to do image retrieval. Experiments are done both on face image databases and texture image database. Compared to other methods, results show that we can get better performance on both face and texture database by using the proposed method

An improved feature vector for content-based image retrieval in DCT domain

International audienceThis paper proposes an improved approach for content-based image retrieval in Discrete Cosine Transform domain. For each 4x4 DCT block, we calculate the statistical information of three groups of AC coefficients and propose to use these information to form the AC-Pattern and use DC coefficients of neighboring blocks to construct DC-Pattern. The histograms of these two patterns are constructed and their selections are concatenated as feature descriptor. Similarity between the feature descriptors is measured by chi-squared distance. Experiments executed on widely used face and texture databases show that better performance can be observed with the proposal compared with other classical method and state-of-the-art approaches

COLOR TEXTURED IMAGE RETRIEVAL BY COMBINING TEXTURE AND COLOR FEATURES

International audienceA new approach for color textured image retrieval based on the combination of color and texture features is proposed. The features are extracted in DCT domain. For texture featuring, Texture-Pattern is proposed to be constructed by using three groups of AC coefficients of each DCT block from the luminance component. And for color featuring, LumaColor-Pattern is constructed by using the DC coefficients from the luminance and chroma components. The histograms of dominant components of these two patterns are constructed and their combination is used for image retrieval. Experimental results on VisTex database have shown that the proposed method yields higher performance than referred approaches which are reported in recently published literature

Analytical modeling of water wave interaction with a bottom-mounted surface-piercing porous cylinder in front of a vertical wall

The interaction of water wave with a bottom-mounted surface-piercing porous cylinder near a rigid vertical wall is investigated by an analytical model newly developed in the present work within the context of linear potential flow theory. The image principle is used to transfer the original problem in bounded water into the equivalent problem of wave interaction with two symmetrical porous cylinders in open seas in the presence of bi-directional incident waves. The velocity potential is analytically derived by means of the eigen function expansion along with the matching technique. Furthermore, a new alternative method for the evaluation of wave force is developed via the application of the Haskind–Hanaoka relation to a porous structure. In this method, an auxiliary radiation potential is introduced to replace the diffraction potential for the calculation of wave force. The auxiliary radiation potential used here is due to the oscillation of a porous cylinder in front of a wall. The image principle is used again to search the solution of the wave radiation problem in bounded water and the original radiation problem is then transferred to that due to two porous cylinders undergoing in-phase orout-of-phase motions in open seas. After the validation of the developed model, detailed parametric study is carried out. The porosity of the cylinder, incident wave heading and spacing between the cylinder and the wall are systematically adjusted to investigate their effects on the wave force as well as the wave elevation. The extension of our model to the case of a cylinder array in front of a wall has also been performed, and the associated phenomenon has been explored

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

International audienceA new approach for image retrieval by combination of colour and texture features is proposed. This approach uses the histogram of feature vectors, which are constructed from the coefficients of some subbands of wavelet transform and chosen according to their intrinsic characters. A K-means algorithm is used to quantise feature vectors. The experimental results both on small size databases (40 classes of textures) and large size databases (167 classes of textures) show that, compared with the state-of-the-art approaches, the proposed approach can achieve better retrieval performance