text: In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21 on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.
  @cite_28: We present a method for learning custom deformation handles for an object, from a co-analysis of similar objects. Our approach identifies the geometric and spatial constraints among the different parts of an object, and makes this information available through abstract shape handles. These handles allow the user to prescribe arbitrary deformation directives including free-form surface deformations. However, only a subset of admissible deformations is enabled to the user as learned from the constraint space. Example applications are presented in shape editing, co-deformation and style transfer.
  @cite_18: The human perception of stylistic similarity transcends structure and function: for instance, a bed and a dresser may share a common style. An algorithmically computed style similarity measure that mimics human perception can benefit a range of computer graphics applications. Previous work in style analysis focused on shapes within the same class, and leveraged structural similarity between these shapes to facilitate analysis. In contrast, we introduce the first structure-transcending style similarity measure and validate it to be well aligned with human perception of stylistic similarity. Our measure is inspired by observations about style similarity in art history literature, which point to the presence of similarly shaped, salient, geometric elements as one of the key indicators of stylistic similarity. We translate these observations into an algorithmic measure by first quantifying the geometric properties that make humans perceive geometric elements as similarly shaped and salient in the context of style, then employing this quantification to detect pairs of matching style related elements on the analyzed models, and finally collating the element-level geometric similarity measurements into an object-level style measure consistent with human perception. To achieve this consistency we employ crowdsourcing to quantify the different components of our measure; we learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from 50K responses to cross-structure style similarity queries provided by over 2500 participants.We train and validate our method on this dataset, showing it to successfully predict relative style similarity with near 90 accuracy based on 10-fold cross-validation.
  @cite_30: We introduce an unsupervised co-hierarchical analysis of a set of shapes, aimed at discovering their hierarchical part structures and revealing relations between geometrically dissimilar yet functionally equivalent shape parts across the set. The core problem is that of representative co-selection. For each shape in the set, one representative hierarchy (tree) is selected from among many possible interpretations of the hierarchical structure of the shape. Collectively, the selected tree representatives maximize the within-cluster structural similarity among them. We develop an iterative algorithm for representative co-selection. At each step, a novel cluster-and-select scheme is applied to a set of candidate trees for all the shapes. The tree-to-tree distance for clustering caters to structural shape analysis by focusing on spatial arrangement of shape parts, rather than their geometric details. The final set of representative trees are unified to form a structural co-hierarchy. We demonstrate co-hierarchical analysis on families of man-made shapes exhibiting high degrees of geometric and finer-scale structural variabilities.
  @cite_2: Texts on methods with introductions Critical glossary of concepts Bibliography Index. Giorgio Vasari - lives of the artists, 1958 Carel van Mander - Dutch and Flemish Painters, 1604 Giovanni Bellori - the idea of the painter, the sculptor and the architect chosen from the higher natural beauties of nature, 1672 Johann Winckelmann - history of ancient art, 1764 Johann Wolfgang von Goethe - Von deutscher Baukunst, 1772 Jakob Burckhardt - reflections on history, 1872 Giovanni Morelli - Italian painters, 1880 Alois Tiegi: late Roman art industry, 1901 Heinrich Wolfflin - principles of art history, 1915 Paul Franki - principles of architectural history, 1914 Roger Fry - reflections, 1920 Henri Focillon - the world of forms, 1934 Alfred H. Baff - chart of modern art, 1936 Erwin Panofshky - the history of art as a humanistic discipline, 1940 Nikolaus Pevsner - an outline of European architecture, 1943 Arnold Hauser - the scope a limitations of sociology of art, 1959 Susan Sontag - against interpretation, 1964 E.H. Gombrich - in search of cultural history, 1967 William Fagg - studying primitive art, 1973 T.J. Clark - the conditions of artistic creation, 1974 John Onians - art history, Kinstegschichte and historia, 1978 Michael Baldwin, Charles Harrison and Mel Ramsden - art history, art criticism and explanation, 1981 Svetlana Alpers - interpretation without representation, or the viewing of Las Meninas, 1983 Hans Belting - the end of the history of art?, 1984 Griselda Pollock - Feminist interventions in the history of Art, 1988.
summary: Comparing the style similarity of two objects or scenes is one of the challenges that have to be addressed when training a machine learning model for interior design or fashion retrieval application. This problem is far from being solved mainly due to the lack of a clear metric defining how to measure style similarity. Various approaches have been proposed for defining style similarity metric. Some of them focus on evaluating similarity between shapes based on their structures @cite_28 @cite_30 and measuring the differences between scales and orientations of bounding boxes. Other approaches propose the structure-transcending style similarity that accounts for element similarity @cite_18 . In this work, we follow @cite_2 , and define style as We enforce this definition by including context information that groups different objects together (in terms of clothing items in an outfit or furniture in a room picture in interior design catalog). This allows us to a take data-driven approach that measures style similarity without using hand-crafted features and predefined styles.