Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrates BGL advances previous works in fine-grained object recognition. An online demo is available at http://www.f-zhou.com/fg_demo/

Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden

With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain

Local Pyramidal Descriptors for Image Recognition

In this paper, we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one's bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement. We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines

African American Parents\u27 Experiences in Their Children\u27s Health Care Encounters

Persistent disparities in African American child health may be the result of the intersection of many social determinants of health and other factors, such as health care relationships. A review of the literature revealed a gap in understanding of African Americans’ perceptions of care; and a gap in understanding of dimensions of relationship-centered care between African American patients and health care providers. The purpose of this qualitative study was to interview African American parents about their encounters with their children’s health care providers; in order to generate new understanding that could lead to interventions that can measurably improve health outcomes for African American children. The author employed an interview guide to interview 18 African American parents in a small south Georgia town about encounters with their children’s health care providers. The data were analyzed within frameworks of social phenomenology, critical ethnography, and intersectionality. Data fell within two main content areas: precursory parental relevances, and we-relationships. Precursory parental relevances included: symbolism of illness and wellness, typifications of health care providers, and various in-group/out-group memberships. Membership in the insurance out-group was particularly important in parents’ perceptions of health care encounters. Parents’ descriptions of we-relationships with children’s health care providers were categorized as routine, problematic, or transformative and were characterized by parents in terms of how patient-centric provider role expectations, relevances, group memberships, and knowledge affected the relationships. Practical recommendations include concrete relationship-centered interventions for health care providers. Recommendations for health professions education include a call for development of cultural competence curriculum with greater emphasis on understanding how health care language, mores, customs, values, codes and practices serve to alienate those persons it purports to serve; and inclusion of intersectionality as a framework for consideration of environmental macrofactors that contribute to health disparities

Narratives by Six Year Old and Nine Year Old Boys: Brute: Institutional, and Non-Institutional Mental Facts

Brute facts, institutional facts, and non-institutional mental facts were studied. The philosophy of constructionism and the theory of intent provided a framework for this research. Intentionality provided the basis for social facts. Brute, institutional, and noninstitutional mental facts were operationally defined. This study analyzed the use of these facts in the narratives of 6-year-old boys and 9-year-old boys. There were a total of 19 participants in this research. This research established brute, institutional, and non-institutional mental facts as appropriate operational categories for studying children\u27s narratives. The 6-year-old boys produced more brute facts than the 9-year-old boys. The 9-year-old boys produced significantly more institutional facts in spontaneous narratives than the 6-year-old boys. The production of non-institutional mental facts was not significantly different between the two groups. The discussion pertained to the ramifications of these results as related to spontaneous language samples, appropriate language sampling size, and the syntagmaticparadigmatic shift