Query-Adaptive
Image Search With Hash Codes
ABSTRACT:
Scalable image
search based on visual similarity has been an active topic of research in
recent years. State-of-the-art solutions often use hashing methods to embed
high-dimensional image features into Hamming space, where search can be
performed in real-time based on Hamming distance of compact hash codes. Unlike
traditional metrics (e.g., Euclidean) that offer continuous distances, the
Hamming distances are discrete integer values. As a consequence, there are
often a large number of images sharing equal Hamming distances to a query,
which largely hurts search results where fine-grained ranking is very
important. This paper introduces an approach that enables query-adaptive ranking
of the returned images with equal Hamming distances to the queries. This is
achieved by firstly offline learning bitwise weights of the hash codes for a
diverse set of predefined semantic concept classes. We formulate the weight
learning process as a quadratic programming problem that minimizes intra-class distance
while preserving inter-class relationship captured by original raw image
features. Query-adaptive weights are then computed online by evaluating the
proximity between a query and the semantic concept classes. With the
query-adaptive bitwise weights, returned images can be easily ordered by
weighted Hamming distance at a finer-grained hash code level rather than the original
Hamming distance level. Experiments on a Flickr image dataset show clear
improvements from our proposed approach.
EXISTING SYSTEM:
While traditional image search engines heavily
rely on textual words associated to the images, scalable content-based search
is receiving increasing attention. Apart from providing better image search
experience for ordinary Web users, large-scale similar image search has also
been demonstrated to be very helpful for solving a number of very hard problems
in computer vision and multimedia such as image categorization.
DISADVANTAGES
OF EXISTING SYSTEM:
An efficient search mechanism is
critical since existing image features are mostly of high dimensions and
current image databases are huge, on top of which exhaustively comparing a
query with every database sample is computationally prohibitive.
PROPOSED SYSTEM:
In this work we represent images using
the popular bag-of-visual-words (BoW) framework, where local invariant image descriptors
(e.g., SIFT) are extracted and quantized based on a set of visual words. The
BoW features are then embedded into compact hash codes for efficient search.
For this, we consider state-of-the-art techniques including semi-supervised
hashing and semantic hashing with deep belief networks. Hashing is preferable
over tree-based indexing structures (e.g., kd-tree) as it generally requires
greatly reduced memory and also works better for high-dimensional samples. With
the hash codes, image similarity can be efficiently measured (using logical XOR
operations) in Hamming space by Hamming distance, an integer value obtained by
counting the number of bits at which the binary values are different. In large
scale applications, the dimension of Hamming space is usually set as a small number
(e.g., less than a hundred) to reduce memory cost and avoid low recall.
ADVANTAGES
OF PROPOSED SYSTEM:
The main contribution of this paper is
the proposal of a novel approach that computes query-adaptive weights for
each bit of the hash codes, which has two main advantages. First, images can be
ranked on a finer-grained hash code level since—with the bitwise weights—each
hash code is expected to have a unique similarity to the queries. In other
words, we can push the resolution of ranking from (traditional Hamming distance
level) up to (hash code level1). Second, contrary to using a single set of weights
for all the queries, our approach tailors a different and more suitable set of
weights for each query. Fig. 1 illustrates the proposed approach.
SYSTEM ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard
Disk - 20
GB
ü Key
Board - Standard
Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE
REQUIREMENTS:
•
Operating system : - Windows XP.
•
Coding Language : C#.Net.
REFERENCE:
Yu-Gang Jiang, Jun Wang, Xiangyang Xue,
and Shih-Fu Chang, “Query-Adaptive Image
Search with Hash Codes”, IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 2,
FEBRUARY 2013.
