Indoor Outdoor Scene Classification in Digital Images

In this paper, we present a method to classify real-world digital images into indoor and outdoor scenes. Indoor class consists of four groups: bedroom, kitchen, laboratory and library. Outdoor class consists of four groups: landscape, roads, buildings and garden. Application considers real-time system and has a dedicated data-set. Input images are pre-processed and converted into gray-scale and is re-sized to “128x128” dimensions. Pre-processed images are sent to “Gabor filters”, which pre-computes filter transfer functions, which are performed on Fourier domain. The processed signal is finally sent to GIST feature extraction and the images are classified using “kNN classifier”. Most of the techniques have been based on the use of texture and color space features. As of date, we have been able to achieve 80% accuracy with respect to image classification

Estimating scene typicality from human ratings and image features

Scenes, like objects, are visual entities that can be categorized into functional and semantic groups. One of the core concepts of human categorization is the idea that category membership is graded: some exemplars are more typical than others. Here, we obtain human typicality rankings for more than 120,000 images from 706 scene categories through an online rating task on Amazon Mechanical Turk. We use these rankings to identify the most typical examples of each scene category. Using computational models of scene classification based on global image features, we find that images which are rated as more typical examples of their category are more likely to be classified correctly. This indicates that the most typical scene examples contain the diagnostic visual features that are relevant for their categorization. Objectless, holistic representations of scenes might serve as a good basis for understanding how semantic categories are defined in term of perceptual representations.National Science Foundation (U.S.) (NSF-CAREER Award (0546262)National Science Foundation (U.S.) (grant 0705677)National Science Foundation (U.S.) (grant 1016862)Nuclear Energy Institute (grant EY02484)National Science Foundation (U.S.) (Career Award (0747120))Google (Firm) (Research Award

SIFT and color feature fusion using localized maximum-margin learning for scene classification

published_or_final_versionThe 3rd International Conference on Machine Vision (ICMV 2010), Hong Kong, China, 28-30 December 2010. In Proceedings of 3rd ICMV, 2010, p. 56-6

Scene categorization with multi-scale category-specific visual words

IS&T/SPIE Conference on Intelligent Robots and Computer Vision XXVI: Algorithms and TechniquesIn this paper, we propose a scene categorization method based on multi-scale category-specific visual words. The proposed method quantizes visual words in a multi-scale manner which combines the global-feature-based and local-feature-based scene categorization approaches into a uniform framework. Unlike traditional visual word creation methods which quantize visual words from the whole training images without considering their categories, we form visual words from the training images grouped in different categories then collate the visual words from different categories to form the final codebook. This category-specific strategy provides us with more discriminative visual words for scene categorization. Based on the codebook, we compile a feature vector that encodes the presence of different visual words to represent a given image. A SVM classifier with linear kernel is then employed to select the features and classify the images. The proposed method is evaluated over two scene classification datasets of 6,447 images altogether using 10-fold cross-validation. The results show that the classification accuracy has been improved significantly comparing with the methods using the traditional visual words. And the proposed method is comparable to the best results published in the previous literatures in terms of classification accuracy rate and has the advantage in terms of simplicity. © 2009 SPIE-IS&T.published_or_final_versio

Multi-layer path planning control for the simulation of manipulation tasks : involving semantics and topology

The industrial and research communities show increasing interest in using automatic path planning techniques for the simulation of manipulation tasks. Automatic path planning, largely explored by the robotics community over the past 30 years, computes the trajectories of robots or manipulated parts. However, as techniques developed so far use mostly purely (and large) geometric models, they may fail, produce a trajectory of little relevance, or lead to very high computation times, when facing complex or very constrained environments. Involving higher abstraction level information should lead to better relevance of the simulation. In this paper, we propose a novel path planning technique relying on an original multi-layer environment model containing geometrical, topological and semantic layers. A first coarse planning step at the topological and semantic layers and a fine planning step at the local and semantically characterized geometrical layer form the path planning process. Experimental full-scale results show increased control on the planning process, leading to much lower computation times and increased relevance of the computed trajectory

Scene categorization with multiscale category-specific visual words

We propose a novel scene categorization method based on multiscale category-specific visual words. The novelty of the proposed method lies In two aspects: (1) visual words are quantized In a multiscale manner that combines the global-feature-based and local-feature-based scene categorization approaches into a uniform framework; (2) unlike traditional visual word creation methods, which quantize visual words from the entire set of training, we form visual words from the training images grouped in different categories and then collate visual words from different categories to form the final codebook. This generation strategy Is capable of enhancing the discriminative ability of the visual words, which is useful for achieving better classification performance. The proposed method is evaluated over two scene classification data sets with 8 and 13 scene categories, respectively. The experimental results show that the classification performance is significantly improved by using the multiscale category-specific visual words over that achieved by using the traditional visual words. Moreover, the proposed method Is comparable with the best methods reported in previous literature in terms of classification accuracy rate (88.81% and 85.05% accuracy rates for data sets 1 and 2, respectively) and has the advantage in simplicity. © 2009 Society of Photo Optical Instrumentation Engineers.published_or_final_versio

What do we perceive in a glance of a real-world scene?

What do we see when we glance at a natural scene and how does it change as the glance becomes longer? We asked naive subjects to report in a free-form format what they saw when looking at briefly presented real-life photographs. Our subjects received no specific information as to the content of each stimulus. Thus, our paradigm differs from previous studies where subjects were cued before a picture was presented and/or were probed with multiple-choice questions. In the first stage, 90 novel grayscale photographs were foveally shown to a group of 22 native-English-speaking subjects. The presentation time was chosen at random from a set of seven possible times (from 27 to 500 ms). A perceptual mask followed each photograph immediately. After each presentation, subjects reported what they had just seen as completely and truthfully as possible. In the second stage, another group of naive individuals was instructed to score each of the descriptions produced by the subjects in the first stage. Individual scores were assigned to more than a hundred different attributes. We show that within a single glance, much object- and scene-level information is perceived by human subjects. The richness of our perception, though, seems asymmetrical. Subjects tend to have a propensity toward perceiving natural scenes as being outdoor rather than indoor. The reporting of sensory- or feature-level information of a scene (such as shading and shape) consistently precedes the reporting of the semantic-level information. But once subjects recognize more semantic-level components of a scene, there is little evidence suggesting any bias toward either scene-level or object-level recognition

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed