FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION

Date

2014-04-25

Author

Ozkan, Savas
Ates, Tayfun
Tola, Engin
Soysal, Medeni
Esen, Ersin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

159
views

0
downloads

In this work, we survey the perormance of various feature encoding models for geographic image retrieval task Recently introduced Vector-of-Locally-Aggregated Descriptors (VLAD) and its Product Quantization encoded binary version VLAD-PQ are compared with the widely used Bag-of-Word (BoW) model. Evaluation results are shown on a publicly available 21-class LULC dataset. With experiments, it is shown that VLAD outperforms classical BoW representation albeit with some increases in the computation time. Additionally, VLAD-PQ results in similar retrieval performance with VLAD but requiring no more computational or memory resources are observed

Subject Keywords

Conferences , Computational Modeling, Signal Processing , Computer Vision , Image Retrieval , Histograms , Pattern Recognition

URI

https://hdl.handle.net/11511/68110

Conference Name

22nd IEEE Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Continuous dimensionality characterization of image structures Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04) Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Object-based image labeling through learning by example and multi-level segmentation Xu, Y; Duygulu, P; Saber, E; Tekalp, AM; Yarman Vural, Fatoş Tunay (Elsevier BV, 2003-06-01) We propose a method for automatic extraction and labeling of semantically meaningful image objects using "learning by example" and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate...
Data-driven image captioning via salient region discovery Kilickaya, Mert; Akkuş, Burak Kerim; Çakıcı, Ruket; Erdem, Aykut; Erdem, Erkut; İKİZLER CİNBİŞ, NAZLI (Institution of Engineering and Technology (IET), 2017-09-01) n the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an object-based semantic image r...
Low-level multiscale image segmentation and a benchmark for its evaluation Akbaş, Emre (Elsevier BV, 2020-10-01) In this paper, we present a segmentation algorithm to detect low-level structure present in images. The algorithm is designed to partition a given image into regions, corresponding to image structures, regardless of their shapes, sizes, and levels of interior homogeneity. We model a region as a connected set of pixels that is surrounded by ramp edge discontinuities where the magnitude of these discontinuities is large compared to the variation inside the region. Each region is associated with a scale that d...
Multiple description coding of animated meshes Bici, M. Oguz; Akar, Gözde (Elsevier BV, 2010-11-01) In this paper, we propose three novel multiple description coding (MDC) methods for reliable transmission of compressed animated meshes represented by series of 3D static meshes with same connectivity. The proposed methods trade off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The methods are based on layer duplication and partitioning of the set of vertices of a scalable coded animated mesh by either spatial or temporal sub...

Citation Formats

S. Ozkan, T. Ates, E. Tola, M. Soysal, and E. Esen, “FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION,” Karadeniz Teknik Univ, Trabzon, TURKEY, 2014, p. 83, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68110.