Multi-scale local pattern co-occurrence matrix for textural image classification

Textural image classification technologies have been extensively explored and widely applied in many areas. It is advantageous to combine both the occurrence and spatial distribution of local patterns to describe a texture. However, most existing state-of-the-art approaches for textural image classification only employ the occurrence histogram of local patterns to describe textures, without considering their co-occurrence information. And they are usually very time-consuming because of the vector quantization involved. Moreover, those feature extraction paradigms are implemented at a single scale. In this paper we propose a novel multi-scale local pattern co-occurrence matrix (MS_LPCM) descriptor to characterize textural images through four major steps. Firstly, Gaussian filtering pyramid preprocessing is employed to obtain multi-scale images; secondly, a local binary pattern (LBP) operator is applied on each textural image to create a LBP image; thirdly, the gray-level co-occurrence matrix (GLCM) is utilized to extract local pattern co-occurrence matrix (LPCM) from LBP images as the features; finally, all LPCM features from the same textural image at different scales are concatenated as the final feature vectors for classification. The experimental results on three benchmark databases in this study have shown a higher classification accuracy and lower computing cost as compared with other state-of-the-art algorithms.

Extraordinary television time travel and the wonderful end to the working day

In this article I will present two arguments. First, the argument that the time travel television series historically provided viewers with a spectacular temporal and spatial alternative to the routine of everyday life, the regulation of television scheduling, and the small-world confines of domestic subjectivity. Taking the decades of the 1970s and 1980s, predominantly in a UK viewing environment, I will suggest that the special effect rendering of the time travel sequence expanded the viewer’s material universe, and affectively wrenched the television set free from the strictures of scheduling and realist programming. Further, the time travel series readily and regularly took the domestic space, the ordinary day and the everyman/ person into awesome environments and situations that suggested alternative lifestyles and behaviours, with a different existential tempo and rhythm. At a narrative, thematic, meta- textual, and aesthetically spectacular level, television time travel saw to the wonderful end of the working day. Case studies include Sapphire and Steal, Dr Who, and Quantum Leap. Second, the article will argue that rather than the contemporary time travel television series being an extraordinary alternative to ordinary life, they instead articulate convergence culture, deregulation, multiple channel viewing, and time-shift culture where there is no such thing as an ordinary working day or domestic viewing context.

Identifying emerging hotel preferences using emerging pattern mining technique

Hotel managers continue to find ways to understand traveler preferences, with the aim of improving their strategic planning, marketing, and product development. Traveler preference is unpredictable for example, hotel guests used to prefer having a telephone in the room, but now favor fast Internet connection. Changes in preference influence the performance of hotel businesses, thus creating the need to identify and address the demands of their guests. Most existing studies focus on current demand attributes and not on emerging ones. Thus, hotel managers may find it difficult to make appropriate decisions in response to changes in travelers' concerns. To address these challenges, this paper adopts Emerging Pattern Mining technique to identify emergent hotel features of interest to international travelers. Data are derived from 118,000 records of online reviews. The methods and findings can help hotel managers gain insights into travelers' interests, enabling the former to gain a better understanding of the rapid changes in tourist preferences.