Approximate Fisher Kernels of Non-iid Image Models for Image Categorization

Download

index.pdf

Date

2016-06-01

Author

Cinbiş, Ramazan Gökberk
Schmid, Cordelia

Metadata

Show full item record

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

Item Usage Stats

135
views

107
downloads

The bag-of-words (BoW) model treats images as sets of local descriptors and represents them by visual word histograms. The Fisher vector (FV) representation extends BoW, by considering the first and second order statistics of local descriptors. In both representations local descriptors are assumed to be identically and independently distributed (iid), which is a poor assumption from a modeling perspective. It has been experimentally observed that the performance of BoW and FV representations can be improved by employing discounting transformations such as power normalization. In this paper, we introduce non-iid models by treating the model parameters as latent variables which are integrated out, rendering all local regions dependent. Using the Fisher kernel principle we encode an image by the gradient of the data log-likelihood w.r.t. the model hyper-parameters. Our models naturally generate discounting effects in the representations; suggesting that such transformations have proven successful because they closely correspond to the representations obtained for non-iid models. To enable tractable computation, we rely on variational free-energy bounds to learn the hyper-parameters and to compute approximate Fisher kernels. Our experimental evaluation results validate that our models lead to performance improvements comparable to using power normalization, as employed in state-of-the-art feature aggregation methods.

Subject Keywords

Statistical image representations, Object recognition, Image classification, Fisher kernels

URI

https://hdl.handle.net/11511/57819

Journal

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

DOI

https://doi.org/10.1109/tpami.2015.2484342

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Image categorization using Fisher kernels of non-iid image models Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2012-01-01) The bag-of-words (BoW) model treats images as an unordered set of local regions and represents them by visual word histograms. Implicitly, regions are assumed to be identically and independently distributed (iid), which is a poor assumption from a modeling perspective. We introduce non-iid models by treating the parameters of BoW models as latent variables which are integrated out, rendering all local regions dependent. Using the Fisher kernel we encode an image by the gradient of the data log-likelihood w....
TRANSFORMATION-INVARIANT DICTIONARY LEARNING FOR CLASSIFICATION WITH 1-SPARSE REPRESENTATIONS Yuzuguler, Ahmet Caner; Vural, Elif; Frossard, Pascal (2014-05-09) Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the design of good dictionaries. In this work, we study the problem of learning class-representative dictionaries from geometrically transformed image sets. In order to efficiently take account of arbitrary geometric transformations in the learning, we adopt a r...
Image annotation with semi-supervised clustering Sayar, Ahmet; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2009) Image annotation is defined as generating a set of textual words for a given image, learning from the available training data consisting of visual image content and annotation words. Methods developed for image annotation usually make use of region clustering algorithms to quantize the visual information. Visual codebooks are generated from the region clusters of low level visual features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this th...
Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation Kayabasi, Alper; Tufekci, Gulin; Ulusoy, İlkay (2023-01-01) Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bias towards seen classes. Taking the former problem into account, our method compares the support feature map with the query feature map at multi scales to become scale-agnostic. As a solution t...
Fine-grained object recognition and zero-shot learning in multispectral imagery Sumbul, Gencer; Cinbiş, Ramazan Gökberk; AKSOY, SELİM (2018-05-05) We present a method for fine-grained object recognition problem, that aims to recognize the type of an object among a large number of sub-categories, and zero-shot learning scenario on multispectral images. In order to establish a relation between seen classes and new unseen classes, a compatibility function between image features extracted from a convolutional neural network and auxiliary information of classes is learnt. Knowledge transfer for unseen classes is carried out by maximizing this function. Per...

Citation Formats

R. G. Cinbiş and C. Schmid, “Approximate Fisher Kernels of Non-iid Image Models for Image Categorization,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 1084–1098, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57819.