Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Data clustering: a review

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

IEEE Transactions on Pattern Analysis and Machine Intelligence

With the rapid increase of multimedia data, textual content in an image has become a very important source of information for several applications like navigation, image search and retrieval, image understanding, captioning, machine translation and several others. Scene text localization is the first step towards such applications and most current methods focus on generating a small set of high precision detectors rather than obtaining large set of detections covering all text patches. In this work we propose a novel hybrid framework for text localization which uses character level recognition recursively in a feedback mechanism to refine text patches and reduce false positives. We use popular MSER algorithm at multiple scales as an initial region proposal algorithm and several filtering stages recursively to improve precision as well as maximize recall. We aim at achieving high recall rather than achieving higher precision since several robust word recognition systems are already available. The word recognition systems are mature enough to produce highly accurate results if provided with maximum amount of regions rather than providing small set of highly precise text patches and losing several other text regions. The main contribution of this paper is the use of character recognizer within a novel feedback mechanism to recursively search for text regions in the neighborhood of previously detected text patches. Using 3 publicly available benchmark datasets (ICDAR2011, MSRA TD-500 and OSTD), we demonstrate the efficacy of the proposed framework for text localization.

Recognition based text localization from natural scene images

In this paper, we discuss a new representation scheme for word images which exploits the structural features. The word image features are represented in the form of a graph called as the geometric feature graph (GFG). The GFG is encoded in the form of a string which serves as a compressed representation of the word image skeleton. We demonstrate reconstruction, and retrieval of word images for 3 different scripts using the GFG string.

Improved geometric feature graph: a script independent representation of word images for compression, and retrieval

The paper presents a novel script independent CRF based inferencing framework for character recognition. In this framework we consider a word as a sequence of connected components. The connected components are obtained using different binarization schemes and different possible sequences are considered using a tree structure. CRF uses contextual information to learn perfect primitive sequences and finds the most probable labeling of the sequence of primitives using multiple hypothesis tree to form the correct sequence of alphabets. This approach is particularly suitable for degraded printed document images as it considers multiple alternate hypotheses for correct decision.

Character Recognition Using Conditional Random Field Based Recognition Engine

We present an integrated scheme for document image compression which preserves the layout structure, and still allows the display of textual portions to adapt to the user preferences and screen area. We encode the layout structure of the document images in an XML representation. The textual components and picture components are compressed separately into different representations. We derive an SVG (scalable vector graphics) representation of the complete document image. Compression is achieved since the word-images are encoded using specifications for geometric primitives that compose a word. A document rendered from its SVG representation can be adapted for display and interactive access through common browsers on desktop as well as mobile devices. We demonstrate the effectiveness of the proposed scheme for document access

An Integrated Scheme for Compression and Interactive Access to Document Images

In this paper, we propose a novel online multi-camera framework for person identification based on gait recognition using Grassmann Discriminant Analysis. We propose an online method wherein the gait space of individuals are created as they are tracked. The gait space is view invariant and the recognition process is carried out in a distributed manner. We assume that only a fixed known set of people are allowed to enter the area under observation. During the training phase, multi-view data of each individual is collected from each camera in the network and their global gait space is created and stored. During the test phase, as an unknown individual is observed by the network of cameras, simultaneously or sequentially, his/her gait space is created. Grassmann manifold theory is applied for classifying the individual. The gait space of an individual is a point on a Grassmann manifold and distance between two gait spaces is the same as distance between two points on a Grassmann manifold. Person identification is, therefore, carried out on-the-fly based on the uniqueness of gait, using Grassmann discriminant analysis.

Santanu Chaudhury

Papers

Recognition based text localization from natural scene images

Improved geometric feature graph: a script independent representation of word images for compression, and retrieval

Character Recognition Using Conditional Random Field Based Recognition Engine

An Integrated Scheme for Compression and Interactive Access to Document Images

Gait Recognition Based Online Person Identification in a Camera Network