Ricky Purnomo

Bio: Ricky Purnomo is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Feature detection (computer vision) & Image retrieval. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Content-Based Image Retrieval Using Regional Representation

Abstract: Representing general images using global features extracted from the entire image may be inappropriate because the images often contain several objects or regions that are totally different from each other in terms of visual image properties. These features cannot adequately represent the variations and hence fail to describe the image content correctly. We advocate the use of features extracted from image regions and represent the images by a set of regional features. In our work, an image is segmented into "homogeneous" regions using a histogram clustering algorithm. Each image is then represented by a set of regions with region descriptors. Region descriptors consist of feature vectors representing color, texture, area and location of regions. Image similarity is measured by a newly proposed Region Match Distance metric for comparing images by region similarity. Comparison of image retrieval using global and regional features is presented and the advantage of using regional representation is demonstrated.

Content-based image retrieval using regional representation

Abstract: Representing general images using global features extracted from the entire image may be inappropriate because the images often contain several objects or regions that are totally different from each other in terms of visual image properties. These features cannot adequately represent the variations and hence fail to describe the image content correctly. We advocate the use of features extracted from image regions and represent the images by a set of regional features. In our work, an image is segmented into homogeneous regions using a histogram clustering algorithm. Each image is then represented by a set of regions with region descriptors. Region descriptors consist of feature vectors representing color, texture, area and location of regions. Image similarity is measured by a newly proposed Region Match Distance metric for comparing images by region similarity. Comparison of image retrieval using global and regional features is presented and the advantage of using regional representation is demonstrated.

Periocular Biometrics in the Visible Spectrum

Abstract: The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.

Periocular biometrics in the visible spectrum: A feasibility study

Abstract: Periocular biometric refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric does not require high user cooperation and close capture distance unlike other ocular biometrics (e.g., iris, retina, and sclera). We study the feasibility of using periocular images of an individual as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set that can be used for matching. The effect of fusing these feature sets is also studied. The experimental results show a 77% rank-1 recognition accuracy using 958 images captured from 30 different subjects.

Nested Partitions Properties for Spatial Content Image Retrieval

Abstract: In this paper, a metric on partitions of arbitrary measurable sets and its special properties for metrical content-based image retrieval based on the ‘spatial’ semantic of images is proposed. This approach considers images represented in the form of nested partitions produced by any segmentations, which are used to express a degree of information refinement or roughening. In doing so, this not only corresponds to rational content control but also ensures creation of specific search algorithms (e.g., invariant to image background) and synthesizes hierarchical models of image search by reducing the number of query and database elements match operations. DOI: 10.4018/978-1-4666-0900-6.ch013

Metrical Properties of Nested Partitions for Image Retrieval

Abstract: Image processing for the efficient retrieval should perform the ability of data granulation and interpretation. In this paper the properties of metric on nested partitions which allow to analyze objects represented at different levels of granularity and abstraction is considered. It also ensures a retrieval of image parts corresponding to the searched objects i.e. provides a search criterion for background independent objects. Index Terms – Image retrieval, metrics, data granulation

Method and device for measuring visual similarity of images

Abstract: The invention relates to a device and a method for measuring visual similarity between two images. One image (Q) being referred to as the model and one image (T) being referred to as the target, the method comprises a prior step (E2) of segmenting the images into regions (Q i , T i ), with each region there being associated at least one attribute (F) representative of at least one characteristic of the region. It furthermore comprises the steps of calculating (E3) the visual similarity between the pairs (Q i , T i ) of possible regions of the two images (Q, T), selecting (E4) a certain number of pairs (Q i , T i ) of regions whose similarity is greater than a first fixed threshold (e), calculating (E9) the global similarity between the two images, based on the pairs (Q i , T i ) of regions selected. According to the invention, the step (E3) of calculating the visual similarity between the pairs (Q i , T i ) of possible regions of the two images (Q, T) takes into account the distance (D(Q i , T i )) between the said attributes (F) of the regions (Q i , T i ) matched and the areas of the regions (Q i , T i ) matched.