Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

Image-based Search and Retrieval for Biface Artefacts using Features Capturing Archaeologically Significant Characteristics

Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors. Through this nuanced approach, we have provided a comprehensive analysis of the strengths and weaknesses of the different descriptors and identified implications for design in the search systems for archaeology

Line segment distribution of sketches for Persian signature recognition

A novel fast method for line segment extraction based on chain code representation of thinned sketches (or edge maps) is presented and exploited for Persian signature recognition. The method has a parallel nature and can be employed on parallel machines. It breaks the macro chains into several micro chains after applying shifting, smoothing and differentiating. The micro chains are then approximated by straight line segments. Length and position distributions of the extracted line segments are used to make a compact feature vector for Iranian cursive signature. The feature vector is invariant under affine transforms and can be used effectively in paperless office projects. Experimental results show fast response and accurate recognition/retrieval rate

Edge image description using angular radial partitioning

The authors present a novel approach for image representation based on geometric distribution of edge pixels. Object segmentation is not needed, therefore the input image may consist of several complex objects. For an efficient description of an arbitrary edge image, the edge map is divided into M/spl times/N angular radial partitions and local features are extracted for these partitions. The entire image is then described as a set of spatially distributed invariant feature descriptors using the magnitude of the Fourier transform. The approach is scale- and rotation-invariant and tolerates small translations and erosions. The extracted features are characterised by their compactness and fast extraction/matching time. They exhibit significant improvement in retrieval performance using the average normalised modified retrieval rank (ANMRR) measure. Experimental results, using an image database initiated from a movie, confirm the supremacy of the proposed method

Sketch-based image retrieval using angular partitioning

This paper presents a novel approach for sketch-based image retrieval based on low-level features. The approach enables measuring the similarity between a full color image and a simple black and white sketched query and needs no cost intensive image segmentation. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Abstract images are obtained using strong edges of the model image and thinned outline of the sketched image. Angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using Fourier transform. The extracted features are scale and rotation invariant and robust against translation. A collection of paintings and sketches (ART BANK) is used for testing the proposed method. The results are compared with three other well-known approaches within the literature. Experimental results show significant improvement in the recall ratio using the new features

Image database retrieval using sketched queries

This paper presents a novel approach for sketch-based image retrieval based on low-level features. It enables the measuring of the similarity among full color multi-component images within a database (models) and simple black and white user sketched queries. It needs no cost intensive image segmentation. Strong edges of the model image and morphologically thinned version of the query image are used for image abstraction. Angular-radial decomposition of pixels in the abstract images is used to extract new compact and affine invariant features. Comparative results, employing an art database (ArT BANK), show significant improvement in average normalized modified retrieval rank (ANMRR) using the proposed features

Image analysis using line segments extraction by chain code differentiation

This paper proposes a new fast method for line segment extraction from edge maps. It has a parallel nature and can be used on parallel machines easily. The method uses the chain codes in the edge map, namely macrochains, for line segment detection. In the first phase, it breaks the macrochains into several microchains by employing the extreme points of the first derivative of shifted-smoothed chain code function. Straight-line segments approximate the resulting microchains. In the second phase, the line segments are grouped together based on their proximity (collinearity and nearness) to make longer segments. The final set could be tailored for any minimum segment length and minimum error desired

Arabic/Persian cursive signature recognition and verification using line segment distribution

This work proposes a fast method for line segment extraction based on chain code differentiation. It is applied to cursive signature recognition of Arabic/Persian. The evaluation method is introduced to obtain a quantitative value for the recognition rate. The comparative results show the existing differences among the methods in recognition, building time and searching time criteria. The two methods used for comparison are invariant moments and CBLSE method

Signature-based document retrieval

This paper presents a new approach for document image decomposition and retrieval based on connected component analysis and geometric properties of the labeled regions. The database contains document images with Arabic/Persian text combined with English text, headlines, ruling lines, trademark and signature. In particular, Arabic/Persian signature extraction is investigated using special characteristics of the signature that is fairly different from English signatures. A set of efficient, invariant and compact features is extracted for validation purposes using angular-radial partitioning of the signature region. Experimental results show the robustness of the proposed method