lsd1508 - an attribute-assisted reranking model

An Attribute Assisted Reranking Model

An Attribute-Assisted Reranking Model for Web Image

Search

ABSTRACT:

Image search reranking is an effective approach to refine the text-based image

search result. Most existing reranking approaches are based on low-level visual

features. In this paper, we propose to exploit semantic attributes for image search

reranking. Based on the classifiers for all the predefined attributes, each image is

represented by an attribute feature consisting of the responses from these

classifiers. A hypergraph is then used to model the relationship between images by

integrating low-level visual features and attribute features. Hypergraph ranking is

then performed to order the images. Its basic principle is that visually similar

images should have similar ranking scores. In this paper, we propose a visual-

attribute joint hypergraph learning approach to simultaneously explore two

information sources. A hypergraph is constructed to model the relationship of all

images. We conduct experiments on more than 1,000 queries in MSRA-MMV2.0

data set. The experimental results demonstrate the effectiveness of our approach.

EXISTING SYSTEM:

Many image search engines such as Google and Bing have relied on

matching textual information of the images against queries given by users.

However, text-based image retrieval suffers from essential difficulties that

are caused mainly by the incapability of the associated text to appropriately

describe the image content.

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The existing visual reranking methods can be typically categorized into three

categories as the clustering based, classification based and graph based

methods.

The clustering based reranking methods stem from the key observation that a

wealth of visual characteristics can be shared by relevant images.

In the classification based methods, visual reranking is formulated as binary

classification problem aiming to identify whether each search result is

relevant or not.

Graph based methods have been proposed recently and received increasing

attention as demonstrated to be effective. The multimedia entities in top

ranks and their visual relationship can be represented as a collection of

nodes and edges

DISADVANTAGES OF EXISTING SYSTEM:

In the classification based methods, visual reranking is formulated as binary

classification problem aiming to identify whether each search result is

relevant or not.

The framework casts the reranking problem as random walk on an affinity

graph and reorders images according to the visual similarities.

PROPOSED SYSTEM:

We propose a new attribute-assisted reranking method based on hypergraph

learning. We first train several classifiers for all the pre-defined attributes




and each image is represented by attribute feature consisting of the responses

from these classifiers.

We improve the hypergraph learning method approach by adding a

regularizer on the hyperedge weights which performs an implicit selection

on the semantic attributes.

This paper serves as a first attempt to include the attributes in reranking

framework. We observe that semantic attributes are expected to narrow

down the semantic gap between low-level visual features and high level

semantic meanings.

ADVANTAGES OF PROPOSED SYSTEM:

We propose a novel attribute-assisted retrieval model for reranking images.

Based on the classifiers for all the predefined attributes.

We perform hypergraph ranking to re-order the images, which is also

constructed to model the relationship of all images.

Our proposed iterative regularization framework could further explore the

semantic similarity between images by aggregating their local

Compared with the previous method, a hypergraph is reconstructed to model

the relationship of all the images, in which each vertex denotes an image and

a hyperedge represents an attribute and a hyperedge connects to multiple

vertices.




SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Floppy Drive : 1.44 Mb.

Monitor : 15 VGA Colour.

Mouse : Logitech.

Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

Operating system : Windows XP/7.

Coding Language : ASP.net, C#.net

Tool : Visual Studio 2010

Database : SQL SERVER 2008




REFERENCE:

Junjie Cai, Zheng-Jun Zha, Member, IEEE, Meng Wang, Shiliang Zhang, and Qi

Tian, Senior Member, IEEE, “An Attribute-Assisted Reranking Model for Web

Image Search”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL.

24, NO. 1, JANUARY 2015.



lsd1508 - an attribute-assisted reranking model

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