LiveSketch: Query Perturbations for Guided Sketch-based Visual Search

LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.Comment: Accepted to CVPR 201

Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.Comment: CIKM 201

Combinatorial locational analysis of public services in metropolitan areas. Case study in the city of Volos, Greece.

Social prosperity largely depends on spatial structure, a relation which becomes stronger in urban areas where the quality of life is menaced by several factors. Traffic, over-building, lack of open space and deficient location of services come to the fore. The latter reflects access inequality and is one of the main reasons for everyday movement difficulties of citizens. Particularly, public services, as part of the public sector, are considered to be driven by the principle of social well-fare. Therefore the study of their location gives rise to the question: how can access of city blocks to public services be evaluated and how can the results of this evaluation be combined with the monetary values assigned by the state? In this respect, the main aim of this paper is the determination of a synthetic methodological framework for the locational analysis and evaluation of public services in urban areas. The proposed approach is based on spatial analysis methods and techniques as well as on the analytical capabilities of GIS and finally leads to the definition of the locational value for each city block. The public services are classified according to served population age groups and to their yearly utilization levels. The minimum and average Manhattan distances to the services of each classification group are calculated along with the percentages of services that are closer than a critical radius to each city block. At the final step, city blocks are classified through the use of cluster analysis to the calculated distances and percentages and then ranked according to their overall accessibility to public services. Their score is utilized in the definition of their locational value and in the formulation of a combinatorial index which compares locational and land values throughout the study area. The methodological framework is applied in the city of Volos where according to the results of the analytical process the majority of city blocks (60,7%) indicates a comparatively lower locational than monetary land value.

Pairwise Quantization

We consider the task of lossy compression of high-dimensional vectors through quantization. We propose the approach that learns quantization parameters by minimizing the distortion of scalar products and squared distances between pairs of points. This is in contrast to previous works that obtain these parameters through the minimization of the reconstruction error of individual points. The proposed approach proceeds by finding a linear transformation of the data that effectively reduces the minimization of the pairwise distortions to the minimization of individual reconstruction errors. After such transformation, any of the previously-proposed quantization approaches can be used. Despite the simplicity of this transformation, the experiments demonstrate that it achieves considerable reduction of the pairwise distortions compared to applying quantization directly to the untransformed data