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1

Self-Supervised Video Hashing with Hierarchical
Binary Auto-encoder

arXiv:1802.02305v1 [] 7 Feb 2018

Jingkuan Song, Hanwang Zhang, Xiangpeng Li, Lianli Gao, Meng Wang and Richang Hong

Abstract—Existing video hash functions are built on three
isolated stages: frame pooling, relaxed learning, and binarization,
which have not adequately explored the temporal order of
video frames in a joint binary optimization model, resulting
in severe information loss. In this paper, we propose a novel
unsupervised video hashing framework dubbed Self-Supervised
Video Hashing (SSVH), that is able to capture the temporal
nature of videos in an end-to-end learning-to-hash fashion. We
specifically address two central problems: 1) how to design an
encoder-decoder architecture to generate binary codes for videos;
and 2) how to equip the binary codes with the ability of accurate
video retrieval. We design a hierarchical binary autoencoder
to model the temporal dependencies in videos with multiple
granularities, and embed the videos into binary codes with less
computations than the stacked architecture. Then, we encourage
the binary codes to simultaneously reconstruct the visual content
and neighborhood structure of the videos. Experiments on two
real-world datasets (FCVID and YFCC) show that our SSVH
method can significantly outperform the state-of-the-art methods
and achieve the currently best performance on the task of
unsupervised video retrieval.
Index Terms—Video Hashing, Video Retrieval, Self-Supervised,
Binary LSTM, Neighbor Model

I. I NTRODUCTION
Nowadays, due to the advances in transmission technologies, capture devices and display techniques, we are witnessing
the rapid growth of videos and video retrieval related services. Take YouTube and Facebook as examples. According
to Youtube Statistics 2017, 300 hours of videos are uploaded
to Youtube every minute, and it attracts over 30 million
visitors per day. For Facebook, it also has 8 billion average
daily video views from 500 million users. Therefore, the
explosive growth of online videos makes large-scale contentbased video retrieval (CBVR) [1], [2], [3] an urgent need.
However, unlike Content-based Image Retrieval (CBIR) that
has been extensively studied in the past decades [4], [5], [6]
and considerable progress has been achieved, CBVR has not
received sufficient attention in multimedia community [7], [8],
[9].
On the other hand, hashing methods have been widely
acknowledged as a good solution for approximate nearest
neighbor search by transforming high-dimensional features as
short binary codes to support efficient large-scale retrieval
and data storage. Therefore, content-based video hashing is
Jingkuan Song, Xiangpeng Li and Lianli Gao are with the Center of Future
Media, School of Computer Science and Engineering, University of Electronic
Science and Technology of China, 611731. Hanwang Zhang is from Nanyang
University of Technology, Singapore. Meng Wang and Richang Hong are with
Hefei University of Technology, Hefei, China.
Manuscript received August 19, 2017.

a promising direction for CBVR, but video is beyond a set
of frames and video retrieval is more challenging than image
hashing. Essentially, the rich temporal information in videos is
a barrier for directly utilizing image-based hashing methods.
However, most current works on video analytics generally
resort to pooling frame-level features into a single video-level
feature by discarding the temporal order of the frame sequence
[2], [3], [10]. Such bag-of-frames degeneration works well
when high-dimensional frame-level features such as CNN
responses [11] and motion trajectories [12] are used, as certain
temporal information encoded in a high dimension can be preserved after pooling. However, for large-scale CBVR, where
hashing (or indexing) of these high-dimensional features as
short binary codes is necessary, the temporal information loss
caused by frame pooling will inevitably result in suboptimal
binary codes of videos. The loss usually takes place in the
process of hash function learning [13], which is a poststep after pooling; Compared to dominant video appearances
(e.g., objects, scenes and short-term motions), nuanced video
dynamics (e.g., long-term event evolution) are more likely to
be discarded as noise in the drastic feature dimensionality
reduction during hashing [14].
Recently, deep learning has dramatically improved the
state-of-the-art in speech recognition, visual object detection
and image classification [15], [16], [17]. Inspired by this,
researchers [18] started to extract from various deep Convolutional Neural Networks (Deep ConvNets) (e.g., VGG
[19] and RestNet [20]) to support video hashing, but video
temporal information is significantly ignored. To capture the
temporal information, the Recurrent Neural Network (RNN) is
used to achieve the state-of-the-art performance in sequential
data streams [21], [22]. RNN-based hashing methods [23]
utilized RNN and video labels to learn discriminative hash
codes for videos. However, human labels are time- and laborconsuming, especially for large-scale datasets. Therefore, we
argue that the key reason to the above defect is that both the
temporal pooling and the hash code learning steps have not
adequately addressed the temporal nature of videos. Also, we
argue that the hash codes which can simply reconstruct the
video content are unable to achieve high accuracy for the task
of video retrieval.
Self-Supervised Temporal Hashing (SSTH) [1] aims to
improve video hashing by taking temporal information into
consideration. A stacking strategy is proposed to integrate
Long Short-Term Memory Networks (LSTMs) [24] with
hashing to simultaneously preserve the temporal and spatial
information of a video using hash codes. Despite the improved
performance, a major disadvantage of stacked LSTM is that it

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introduces a long path from the input to the output video vector
representation, therefore may result in heavier computational
cost.
Therefore, to address the issues of leveraging temporal
information and reducing computational as costs as mentioned
above, we improve the version of SSTH from [1] by proposing
Self-Supervised Video Hashing (SSVH), which encodes video
temporal and visual information simultaneously using an endto-end hierarchical binary auto-encoder and a neighborhood
structure. It is worth to highlight the following contributions.
• We develop a novel LSTM variant dubbed Binary LSTM
(BLSTM), which serves as the building block of the
temporal-aware hash function. We also develop an efficient backpropagation rule that directly tackles the
challenging problem of binary optimization for BLSTM
without any relaxation.
• We design a hierarchical binary auto-encoder to model
the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with
less computations. To achieve accurate video retrieval, we
impose the binary codes to simultaneously reconstruct the
visual content and neighborhood structures of the videos.
• We successfully show that our SSVH model achieves
promising results on two large-scale video dataset FCVID
(91, 223 videos) and YFCC (700, 882 videos). Experimental results on two datasets show superior performance
compared to other unsupervised video hashing methods.
Compared with SSTH, we use hierarchical structure to
replace stacked LSTMs which can efficiently learn long-range
dependency and reduce computational cost. Besides, we also
use a neighborhood structure to enhance the representation
ability of binary codes. Experimental results verify the effectiveness of our proposed method.
II. R ELATED WORK
In this section, we describe related work. Our SSVH is
closely related to hashing in terms of the algorithm, and video
content analysis in terms of the application.
A. Hashing
The rapid growth of massive databases in various applications has promoted the research and study of hash-based
indexing algorithm. Learning to hash [25] has been widely
applied to approximate nearest neighbor search for large-scale
multimedia data, due to its computation efficiency and retrieval quality. Learning-based hashing learns a hash function,
y = h(x) ∈ {0, 1}L , mapping an input item x to a compact
code y. By mapping data into binary codes, efficient storage
and search can be achieved due to the fast bit XOR operations
in Hamming space. Current hashing methods can be generally
categorized into supervised and unsupervised ones.
Supervised hashing methods [23], [2], [18], [26], [6] are
proposed to utilize available supervision information like class
labels or pairwise labels of the training data for improving
the performance of hash codes. Ye et. al. [2] proposed a
supervised framework Video Hashing with both Discriminative commonality and Temporal consistency (VHDT) with

2

structure learning to design efficient linear hash functions
for video indexing and formulate the objective function as
a minimization problem of a structure-regularized empirical
loss function. But it only generated frame-level codes. Cao et.
al. [27] proposed a novel deep learning architecture named
HashNet to learn hash codes by a continuation method, which
learned exactly binary codes from imbalanced similarity labels
but ignored the temporal information of video frames. Deep
Pairwise-Supervised Hashing (DPSH) [26] took into account
the pairwise relationship and proposed a novel deep hashing
method to perform simultaneous features extraction and hash
codes learning. Supervised Recurrent Hashing (SRH) [23] was
proposed to deploy the Long Short-Term Memory (LSTM)
to model the structure of video samples and introduce a
max-pooling mechanism to embedding the frames into fixedlength representations that are fed into supervised hashing
loss. In addition, Liong et.al [18] proposed a method named
Deep Video Hashing (DVH), which utilized spatial-temporal
information after the stacked convolutional pooling layers
to extract representative video features, and then obtained
compact binary codes.
Unsupervised hashing methods [5], [4], [28], [1], [3], [29]
often utilize the data properties such as distribution or manifold structure to design effective indexing schemes. ITQ [5]
rotated data points iteratively to minimize the quantization
loss. Liong et.al [28] proposed to use deep neural network
to learn hash codes by three objective: (1) the loss between
the real-valued feature descriptor and the learned binary codes,
(2) binary codes distribute evenly on each bit and (3) different
bits are as independent as possible. Most of the proposed
methods are devoted to image retrieval, which cannot be
directly applied to video hashing due to its inherent temporal
information. A more completed survey of hashing methods
can be found in [30].
There are also some research focusing on video hashing. For
example, the hash functions proposed by Song et al. [3] and
Cao et al. [27] are unaware of the temporal order of video
frames. Ye et al. [2] exploited the pairwise frame order but
their method requires video labels and only generated framelevel codes. Although Revaud et al. [31] exploited the shortterm temporal order, their video quantization codes were not
binary. In contrast to the above methods, our SSVH is an
unsupervised binary code learning framework that explicitly
exploits the long-term video temporal information.
B. Video Content Analysis based on LSTM
A video is a sequence of frames. But it is beyond a set of
frames and the temporal information in video is important
for video content analysis. To capture the temporal order,
Ng et al. [32] introduced Long Short-Term Memory (LSTM)
inspired by the general sequence to sequence learning neural
model proposed by Sutskever et al. [33]. Since then, LSTM
has been widely used in different research fields such as
image and video captioning [17], [21], [34], nature language
processing [35], [36] and visual question-answering [37], [38].
The basic video content analysis model is an encoder-decoder
framework composed of LSTM. They usually use deep neural

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Recon. Losses
+ Neighbor Loss
Reconstructed
Backward Reconstructed
Video Level Feature
Frame Features

vM 1

vM

Backward Hierarchical
Binary Decoder

LSTM

LSTM

LSTM

LSTM

LSTM

Hierarchical
Binary Encoder

Hash Code

LSTM

v2

LSTM

v1

LSTM

LSTM

vM

BLSTM

LSTM

vM 1
LSTM

LSTM

v1

v2

BLSTM

LSTM
LSTM

{v 1 , v 2 , , v i , , v M }

Forward Hierarchical
Binary Decoder

Self-Supervised Video Hashing

Testing Videos
SSVH
based
Video
Retrieval

LSTM

LSTM

{v1 , v 2 , , vi , , v M }

BLSTM

v1

Forward Reconstructed
Frame Features

LSTM

v2

LSTM

vM 1

LSTM

Global Hierarchical
Binary Decoder

vM

Frame Level
Features

LSTM

{v1, v2 , , vi , , vM }

vg
vg

Training Videos

Trained SSVH Hash
Model

001101

001100

111110

Query Item

110011

110000

110110

Dataset Hash Codes

Result List

Fig. 1. The overview of the proposed Self-Supervised Video Hashing for content based video retrieval. The left part with light blue background denotes
the off-line training with reconstruction losses and a neighborhood structure loss. The right part with the gray background denotes the content-based video
retrieval process.

network as an encoder to extract frame-level features. Vinyals
et al. [39] proposed to use LSTM to decode the latent
semantic information of images and generate words in order.
Sequence to sequence model [21] used a two-layer stacked
LSTM to encode and decode the static and motion information
to generate the video caption. Hierarchical Recurrent Neural
Encoder [22] was able to exploit video temporal structure in a
longer range by reducing the length of input information flow.
Hierarchical LSTM layer was able to uncover the temporal
transitions between frame chucks with different granularities
and can model the temporal transpositions between frames
as well as the transitions between segments. Different from
the stacked LSTM which simply aims to introduce more
non-linearity into the neural model, the Hierarchical LSTM
aimed to abstract the visual information at different time
scales, and learn the visual features with multiple granularities.
Song et al. [34] proposed a hierarchical LSTM with adaptive
attention to generate caption. This model used LSTM to
encode video content and semantic information, and decode
video information by adaptive attention to generate video
caption. SAN (Stacked Attention Network) were proposed
with CNN and RNN as an encoder to explore visual structure.
Encoder-decoder framework composed of LSTM is popular
in sequential data understanding and makes a great success.
We also choose LSTM as a basic unit in our encoder-decoder
model.

III. P ROPOSED M ETHOD
In this section, we formulate the proposed Self-Supervised
Video Hashing framework (Fig. 1). Notations and problem
definition that will be used in the rest of the paper will be
introduced first. Then we will present two major components
of our novel framework, Hierarchical Binary Auto-Encoder
and Neighborhood Structure. Finally, optimization method is
introduced to train our model.

A. Notations and Problem Definition
Given a video V = [v1 , ..., vM ] ∈ RM ×D , where M is
the number of frames in each video1 , and D is the feature
dimensionality of each frame. N denotes the number of videos
in dataset. The features for each frame in a video are extracted
as a pre-processing step, and vi indicates the i-th frame
features of a video. The goal of a video hashing method is
to learn a binary code b ∈ {−1, 1}L for each video where L
is the code length. h is the hidden state of BLSTM unit before
sgn function, and b = sgn(h).
B. Hierarchical Binary Auto-Encoder
Long Short-Term Memory (LSTM) is popular in video
analysis in recent years because of the effectiveness of processing sequential information. However, the original LSTM
can only generate continuous values instead of binary codes.
A straightforward way is to add a hashing layer which consists
of a full connected layer to obtain the hidden variable h, and
a sgn activation layer to binarize the h to binary codes b.
However, as pointed out in [1], this strategy is essentially based
on frame pooling, where the pooling function is an RNN. Even
though the pooling is temporal-aware, the hash codes do not
directly capture the temporal nature of videos.
In order to design an architecture that can simultaneously
capture the temporal information of videos and generate binary
codes, we propose a novel hierarchical binary auto-encoder
based on hierarchical LSTM [22]. Specifically, our architecture
consists of an encoder and a decoder, while the decoder is
divided into forward hierarchical binary decoder, backward
hierarchical binary decoder and global hierarchical binary
decoder.
1) Hierarchical Binary Encoder: As can be seen in Fig. 2,
the binary encoder is a two-layered hierarchical RNN structure, and it consists of vanilla LSTM and binary-LSTM. The
1 For

each video, we extract equal number of frames

LSTM

LSTM

LSTM

LSTM will be used as input to i-th time step in the second
BLSTM layer. Our BLSTM follows a similar data flow as
LSTM, and the detailed implementation of BLSTM is given
as follows:
ft = σ(Wzf zt + Ubf bt−1 + Mcf ◦ ct−1 + bf )

(2)

it = σ(Wzi zt + Ubi bt−1 + Mci ◦ ct−1 + bi )

(3)

ot = σ(Wzo zt + Ubo bt−1 + Mco ◦ ct−1 + bo )

(4)

mt = φ(Wzm zt + Ubm mathbf bt−1 + bm )

(5)

ct = batch_norm(ft ◦ ct−1 + it ◦ mt )

(6)

ht = ot ◦ ct

(7)

bt = sgn(ht )

(8)

LSTM

LSTM

LSTM
LSTM

LSTM

LSTM

LSTM

LSTM

LSTM
LSTM
LSTM

BLSTM

LSTM

LSTM

v

LSTM

LSTM

BLSTM

BLSTM

b

vi
vM
v2
v3
Hierarchical Binary Encoder

LSTM

LSTM

v1

LSTM

vg

Backward Hierarchical Binary Decoder
v2
v1
vM
vM  2
vM 1

LSTM

Global Hierarchical
Binary Decoder

4

LSTM

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v2
v3
vM
vM 1
Forward Hierarchical Binary Decoder

Fig. 2. The overview of encoder-decoder. The input video V = [v1 , ..., vM ]
is firstly mapped to binary codes by the Hierarchical Binary Encoder. Then, we
reconstruct the video using Forward Hierarchical Binary Decoder, Backward
Hierarchical Binary Decoder and Global Hierarchical Binary Decoder.

Fig. 3. The difference between a stacked LSTM and a hierarchical LSTM.
The above framework means a stacked architecture and the below framework
is a hierarchical structure (with stride=2). The basic unit usually denotes a
RNN unit (LSTM or GRU) in the encoder-decoder framework.

first layer is an original LSTM which can be considered as a
higher-level feature extractor for the frames. The hidden state
at time step i is zi :
zi = f (vi , zi−1 )

(1)

where vi indicates the features of the i-th frame, zi−1 is the
hidden state at time step i − 1 (z0 = 0) and f(·) is the function
of this LSTM layer. The hidden state is used as input to the
second layer (BLSTM).
The second layer is a binary LSTM layer (Fig. 2), which
embeds the higher-level real-valued features of a video to
a binary code. To achieve this, a straightforward way is
stacking another layer, as introduced in [1]. However, it
will increase computation operations. Inspired by [22], we
proposed a hierarchical binary LSTM. Different from stacked
LSTM [22], in the hierarchical binary LSTM, not all the
output of the first-layer LSTM is connected to the secondlayer BLSTM. The motivation is that this hierarchical structure
can efficiently exploit video temporal structure in a longer
range by reducing the length of input information flow, and
compositing multiple consecutive frames at a higher level.
Also, computation operations are significantly reduced. The
difference between a stacked LSTM and a hierarchical LSTM
is illustrated in Fig.3.
Suppose the stride of the hierarchical BLSTM is l. Then,
only the output zl×i of the (l ×i)-th time step in the first layer

where ◦ denotes the element-wise multiplication and
batch_norm means batch normalization. Therefore, the output
of our encoder will be a binary code b ∈ {−1, 1}L . The
behaviors of “forget”, “input” and “output” in BSLTM unit
are respectively controlled by three gate variables: forget gate
ft , input gate it , and output gate ot . W, U and M denote the
shared weight matrices of BLSTM to be learned and b means
bias term. mt is the input to the memory cell ct , which is
gated by the input gate it . σ denotes the element-wise logistic
sigmoid function and φ denotes hyperbolic tangent function
tanh.
2) Forward Hierarchical Binary Decoder: The forward hierarchical binary decoder reconstructs the input frame features
in a forward order v̄1 , v̄2 , ..., v̄M using the binary codes b.
The decoder also has a hierarchical structure which consists
of two layers of LSTM. Specifically, the hidden state at time
step i of the first layer LSTM is z̄i :
z̄i = f¯(z̄i−1 , 0)

(9)

where f¯(.) is the function for the forward LSTM, z̄i−1 is the
hidden state at time step i − 1 and z̄0 = b.
Similarly, the output z̄i in the first layer LSTM will not be
connected to all the units in the second layer LSTM. Suppose
we have the same stride of l. Then, the output z̄i of the i-th
time step in the first layer LSTM will be used as input to i×lth time step in the second LSTM layer. The reconstructed z̄0 j
is formulated as:
z̄0 j = f¯0 (z̄0 j−1 , z¯◦ j )

(10)

where f¯0 (.) is the function for the second layer of the forward
LSTM, z̄0 j−1 is the hidden state at time step j − 1 of the
second layer LSTM (z̄0 0 = 0), z¯◦ j = z̄(j−1)/l+1 if j − 1 is a
multiple of l and z¯◦ j = 0 otherwise.
Then the reconstructed features will be attained by linear
reconstructions for the output of the decoder LSTMs:
v̄j = W̄ × z̄0 j + r̄

(11)

where W̄ is weight matrix and r̄ means the bias.
We can define the forward decoder loss of a video as the Euclidean distance of the original features and the reconstructed
features as:
M
X
Lossf =
||vt − v̄t ||2
(12)
t=1

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(15)

where W̃ is weight matrix and r̃ means the bias.
The backward decoder loss is defined as:
1
X

Lossb =

||vt − ṽt ||2

(16)

t=M

4) Global Hierarchical Binary Decoder: Apart from the
forward and backward hierarchical binary decoder, we also
use a global hierarchical decoder to reconstruct the video level
features. Here, we use mean-pooling of all the frame-level
features as the video-level feature. The LSTM used in both
layers are two basic LSTMs. The global reconstruction loss
of a video is defined as:
Lossg = ||vg − ṽg ||2

(17)

PM

1
vg = M
j=1 vi indicates the mean of all frame-level
features of a video.
5) Hierarchical Binary Auto-Encoder Loss Function: The
hierarchical binary auto-encoder consists of three components,
and the reconstruction loss is also composed of forward loss,
backward loss and global loss, which is defined as:

ReconLoss = Lossf + Lossb + Lossg

LSTM

LSTM

LSTM

LSTM
bi
sign
hi ,

BLSTM

Neighbor
Similarity
Construction

bi hi ,
b j hj,

si , j

Neighbor Loss

(14)

where f˜0 (.) is the function for the second layer of the
backward LSTM, z̃0 j+1 is the hidden state at time step M − j
of the second layer LSTM (z̃0 0 = 0), z˜◦ j = z̃(M −j)/l+1 if
(M − j) is a multiple of l and z˜◦ j = 0 otherwise.
Then the reconstructed features will be attained by linear
reconstructions for the output of the decoder LSTMs:
ṽj = W̃ × z̃0 j + r̃

BLSTM Unit

Hierarchical Binary Encoder

vj

(13)

where f˜(.) is the function for the backward LSTM, z̃i−1 is
the hidden state at time step i − 1 and z̄0 = b.
The reconstructed ṽj is formulated as:
z̃0 j = f˜0 (z̃0 j+1 , z˜◦ j )

vi

BLSTM

z̃i = f˜(z̃i−1 , 0)

Videos

LSTM

3) Backward Hierarchical Binary Decoder: The backward
hierarchical binary decoder is similar to the forward hierarchical decoder. It reconstructs frame-level features in a reverse
order, i.e., ṽM , ..., ṽi , ṽ1 . Specifically, the hidden state at time
step i of the first layer LSTM is z̃i :

(18)

C. Neighborhood Structure
We argue that achieving a good quality of video content
reconstruction is not enough to equip the binary codes with the
ability of accurate video retrieval. Using basic reconstruction
loss, we can learn a binary code of a video, which can only
reconstruct the video. Lots of previous studies shows that it
is beneficial to exploit the data structure for learning a lowdimensional embedding for the retrieval task. Neighborhood
structure enforces similar videos to have close binary codes
and dissimilar videos to have different binary code. Inspired by
this, we propose a novel method to exploit the neighborhood
structure of videos. Then we can train our model to encourage
the binary codes to preserve this neighborhood structure.

Fig. 4. The overview of the neighborhood loss. We first construct the
neighborhood structure of the videos. Then, the videos are sent to hierarchical
LSTM and generate corresponding binary codes. Finally, the neighborhood
loss is calculated based on the neighborhood structure and binary codes.

1) Neighborhood Structure Construction: We construct this
neighborhood structure as a preprocessing step.2 First, we use
VGG [19] network to extract frame-level features of videos
{vi,k }M
to get a video-level repk=1 , and use mean-pooling
PM
1
resentation Vi = M
v
.
We
expect that neighboring
i,k
k=1
videos have similar video-level representations. We use cosine
similarity to measure the similarity between any two videos i
and j:
CosSimi,j =

Vi T · Vj
2

2

kVi k kVj k

(19)

For each video, we find its K1 -NN and store their indexes
in P ∈ RN ×K1 . If we set K1 to a small number, there
are not enough neighboring information to preserve for our
large dataset. On the other hand, if we simply increase K1 ,
the accuracy of the retrieved neighbors drops. Therefore, we
design a new strategy to obtain more neighborhood structure
information. Specifically, we compute the intersection of a
video’s most relevant videos’ indexes then we get the top
K2 indexes based on the size of intersection. Every video
in the K2 indexes shares at least one common neighbor with
the video. For example, after retrieving K1 -NN, we get the
relevant video indexes of video i as {1, 2, 3, 4, 5} and the
relevant video indexes of video j as {1, 3, 5, 7, 9}. If the size
of intersection is among the top K2 -NN of video i, all videos
in the intersection will be neighbors of video i, which means
{1, 2, 3, 4, 5, 7, 9} will be regarded as neighbors of video i. We
construct the similarity matrix S ∈ RN ×N by preprocessing
the train data as mentioned above. If si,j = 1, video i and
video j are considered as neighbors. And si,j = −1 means
video i and video j are not neighbors.
Neighborhood Structure Loss: As described in Sec.III-B,
we can get the binary codes B = {bi }L from binary encoder
2 While we can also construct the neighborhood structure directly using the
features during the learning of our neural network, without this preprocessing
step of feature extraction, we found that the construction of neighborhood
structure is time-consuming, and the updating of neighborhood structures
based on the updating of video features in each epoch does not have significant improvement on the performance. Therefore, we fix this neighborhood
structure calculated based on the features.

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for all the videos. Then we define a loss function based on
the neighborhood structure as:
X

min L =

si,j ∈S

2
1
( bTi bj − si,j )
L

p(h)
1

(20)

where bi , bj are the hash codes of videos i and j, si,j indicates
the similarity of video i and j. bi = sgn(hi ), and hi is the
hidden state of encoder before binarization of video i as in
Eq.8. Instead of defining the loss function on the binary codes
bi and bj , we put the constraints of neighborhood structure
preserving on the hi and hj . Then, we have the following
regularized problem by replacing the equality constraint in
Eq.20 by a regularization terms as:

h
-1
Fig. 5. Approximated sgn function using p(h). Illustrative process of how
p(h) (black line) approximates sgn(h) (red line).

to deal with this binary optimization
imated sgn function as Fig.5:

 −1,
sgn(h) ≈ p(h) =
h,

1,

problem and use approx-

2
1
( hTi hj − si,j ) + η(||bi − hi ||)2
for h < −1
L
si,j ∈S
for −1 ≤ h ≤ 1
(21)
for h > 1
where η is weight of regularization term. This term impels
neighboring videos to have similar hash codes. The overview Then we can get the derivative of sgn(h) as :
is illustrated in Fig.4. The final loss function is composed of
sgn0 (h) := p0 (h) = 1(|h| ≤ 1)
the reconstruction loss as Eq.18 and pairwise loss as Eq.21:

N eighborLossi,j =

Loss = λ×

N
X

X

ReconLossi +(1 − λ)×

i=1

N
X

N eighborLossi,j

i,j=1

(22)
λ is a hyper parameter of our model which balances the
reconstruction loss and neighbor loss.
D. Optimization Method

Then bi is used to reconstruct video frame features in forward,
backward and global mode.
v̄i = F orwardDecoder(θdf , bi )

(24)

ṽi = BackwardDecoder(θdb , bi )

(25)

vi,g = GlobalDecoder(θdg , bi )

(26)

As illustrated in Sec.III-B, we can re-write the reconstruction
loss as:
M
X
t=1

(29)

The derivative p0 (x) states a simple back-propagation rule
for BLSTM: when the gradients back propagate to the sgn
function, we only allow gradients, whose neural response
are between -1 and +1, to pass through. Note that we can
also utilize other functions, e.g., tanh(h) to approximate
sgn(h). We will evaluate the performance differences in the
experiments.
E. Comparison with SSTH

In this section, we will formulate our loss function and
come up with a scheme to train our model. Suppose that
θe denotes the parameters of hierarchical binary encoder. θd
denotes the parameters of decoder composed of the forward
decoder parameters θdf , backward decoder parameters θdb and
global decoder parameters θd g. Binary codes can be obtained
by encoder as:
bi = Encoder(θe , vi )
(23)

ReconLoss =

(28)

1
X
||vi,t − v̄i,t ||2+
||vi,t − ṽi,t ||2+||vi,g −ṽi,g ||2
t=M

(27)

We can update the parameters θe , θd f , θdb and θdg by
utilizing back propagation to optimize our model.
However, training SSVH equipped with BLSTM is essential
NP-hard as it involves binary optimization of the hash codes
that requires combinatorial search space. We follow SSTH [1]

We improve the version of SSTH from [1] by defining a
hierarchical recurrent structure rather than using a stacked
structure. Despite of the improved performance of SSTH, a
major disadvantage of stacking is that it introduces a long
path from the input to the output video vector representation,
thereby resulting in heavier computational cost. Compared
with SSTH, SSVH proposed dramatically shortens the path
with the capability of adding non-linearity, providing a better
trade-off between efficiency and effective. In other words,
SSVH extracts the video information at different time scales,
and learns the video hash codes with multiple granularities.
Moreover, in [1], the encoder RNN with BLSTM runs through
the sequence, generating a set of hash codes and then the
decoder RNN decodes them to reconstruct the frame-level
feature sequence in both forward and reverse orders. Compared with SSTH, we reconstruct the video by not only with
forward and backward hierarchical decoders but also with a
new global hierarchical binary decoder. Global reconstruction
ensure the accuracy of reconstruct appearance information. In
addition, we propose a neighbor structure to further improve
the performance. It makes similar videos have similar hash
codes and different videos have different hash codes.
IV. E XPERIMENTS
All our experiments for SSVH are conducted with
Theano [40] on a NVIDIA TITAN X GPU server. Our model

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can be trained at the speed of about 32 videos per second with
a single TITAN X GPU.
A. Datasets and Setting
We choose the popular FCVID [41] and YFCC [42] to
evaluate the performance of our model.
FCVID: FCVID is a large video dataset named FudanColumbia Video Dataset containing 91,223 web videos annotated manually according to 239 categories. The categories
in FCVID cover a wide range of topics like social events
(e.g. “tailgate party”), procedural events (e.g. “making cake”),
objects (e.g. “panda”), scenes (e.g. “beach”), etc. We download
91,185 videos from this dataset because of some damaged
videos. This dataset is split into training set containing 45,585
videos and test set containing 45,600 videos. We use the
training set for unsupervised learning and the test set as
retrieval database and queries.
YFCC: Yahoo Flickr Creative Commons 100 Million
Dataset is a huge collection of multimedia data. There are
0.8M videos in this dataset officially. But we only collected
700,882 videos because of invalid url and corrupted videos.
We select 511,044 videos as our dataset from YFCC. We use
the 101,256 labeled videos as in [1] for retrieval and the left
409,788 unlabeled data as training data.
B. Implementation Details
In this section, we will introduce some implementation
details of our model. For each video, we get equally-spaced
24 frames and we think that is enough to represent a video.
We use VGG [19] network to extract the frame-level features
in our experiment and obtain the 4096-d features as the input
of our model. The stride of hierarchical auto-encoder is 2 and
the second layer of decoders has 12 units. Due to the huge
scale of YFCC, we can not construct a neighbor similarity
matrix of 400K×400K. We split the training data into 9 parts
because of the limitation of memory and each part have around
45K videos. For each part, we get their neighbor structure
matrix. Then we train our model in order. Some videos in
YFCC (around 50K) do not belong to the 80 categories, and
following the instructions of the data provider, we regard these
unlabeled videos as the “others”.
During the training, we use Stochastic Gradient Descent
(SGD) algorithm to do parameters updating with a mini-batch
size of 256. The regularization parameters are set as η = 0.2
in Eq.21 and λ = 0.001 in Eq.22. In neighborhood structure,
we choose K1 as 20 and K2 as 10. To compare with baseline
methods, we use the publicly available codes and run them on
both FCVID and YFCC.
C. Evaluation Metrics
We adopted Average Precision at top K retrieval
videos(AP@K) for retrieval performance evaluation [43]. AP
means the average of precisions at each correctly retrieved data
point. R denotes the number of total relevant videos. Ri means
the number of relevant videos, Ii = 1 means the retrieved
video is relevant and Ii = 0 otherwise. AP@K is defined as

7

1
min(R,K)

PK

Ri
i=1 i

×Ii . We use the whole test video set as the
queries and database. Then we can obtain mAP@K by taking
the mean of AP@K of all queries. Hamming ranking is used
as the search protocol.
D. Components and Baseline Methods
In this subsection, we aim to investigate the effect of
each component in our framework. Here we introduce some
combinations of the components:
• FB (Forward and Backward Reconstruction): FB consists
of forward and backward reconstruction loss Lossf and
Lossb as illustrated in Fig.1. FB reconstructs the framelevel features and trains the hash functions simultaneously.
• FB + GR (FB + Global Reconstruction (GR)): Based
on forward and backward reconstruction loss, we add the
global reconstruction loss Lossg .
• Neighborhood Structure. Neighborhood structure loss
N eighborLoss preserves the neighborhood structure in
the original space, as is illustrated in Sec.III-C.
• SSVH. SSVH is composed of the Lossf , Lossb , Lossg
and N eighborLoss, which is illustrated in Fig. 1.
• FB + GR + GTHNS.(FB + GR + Groundtruth Neighborhood Structure) Here, we compute the neighbor similarity
using the labels of training data to compare with our
SSVH.
We also compare our method with several the state-of-the-art
unsupervised hashing methods to validate the performance of
our method. These methods are:
ITQ. Iterative Quantization (ITQ) [5] is a representative unsupervised hashing method for image retrieval, and we extend
it for video retrieval. We get a video-level feature by applying
mean-pooling on the frame-level features.
Submod. Submodular Video Hashing (Submod) [10] is also a
common video retrieval method. We first use mean pooling
to extract video representation then hash it into a 1024dimension code using traditional hashing method LSH. Then
we measure the informativeness of training data to select k
most informative hash functions.
MFH. Multiple feature hashing (MFH) [3] learns hash functions based on the similarity graph of the frames. It learns
frame-level hash codes and uses average pooling to get the
real-valued video-level representation, followed by binarization to get the hash codes.
DH. Deep hashing (DH) [28] learns hash functions based on
a deep neural network by adding a binarization loss. We use
original encoder-decoder model to extract video representations by getting the output of the encoder.
SSTH. Self-Supervised Temporal Hashing (SSTH) [1] means
Self-Supervised Temporal Hashing, which trains hash functions as an auto-encoder to reconstruct the frame features in
forward and backward order.
E. Performance Analysis
1) Effect of Components of SSVH: We first test different
combinations of the components of our SSVH on the FCVID

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8

TABLE I
E FFECT OF C OMPONENTS OF SSVH ON THE FCVID DATASET ( CODE LENGTH =256).
mAP@K
SSTH
FB(Forward+Backward)
FB + GR(Global Reconstruction)
Neighborhood Structure
FB + GR + Neighborhood Structure
FB + GR + GroundTruth

20
28.37%
30.90%
31.72%
36.51%
37.92%
42.41%

40
24.12%
24.52%
25.36%
32.40%
33.40%
39.27%

60
21.26%
21.25%
22.09%
30.15%
30.92%
37.67%

80
19.51%
19.05%
19.87%
28.40%
29.00%
36.38%

100
17.93%
17.38%
19.18%
26.82%
27.29%
35.17%

TABLE II
MAP RESULTS ( I . E ., TOP 5, 10, 20, 40, 60, 80 AND 100 RETRIEVAL RESULTS ) ON THE FCVID DATASET OF 256 BITS . T HE PERFORMANCE VARIANCE
WITH DIFFERENT PARAMETERS : K1 OR K2.

K2=10

K1=10

K1
5_10
10_10
20_10
30_10
40_10
50_10
K2
10_5
10_10
10_20
10_30
10_40
10_50

5
52.76%
54.75%
53.10%
52.08%
51.31%
50.19%
5
53.03%
54.75%
53.18%
52.13%
51.47%
51.18%

10
42.46%
45.53%
44.09%
43.06%
42.19%
40.37%
10
42.82%
45.53%
44.19%
43.00%
42.26%
41.18%

20
35.21%
39.25%
38.10%
37.06%
36.13%
35.78%
20
35.67%
39.25%
38.25%
36.99%
36.25%
35.72%

0.35

mAP

0.30
FB+GR
FB

0.25
0.20
0.15

0

20

40
60
Epoch

80

100

40
29.66%
34.39%
33.63%
32.56%
31.72%
31.03&
40
30.09%
34.39%
33.74%
32.56%
31.85%
31.22%

60
26.76%
31.71%
31.19%
30.12%
29.40%
28.63%
60
27.16%
31.71%
31.26%
30.15%
29.52%
28.85%

80
24.70%
29.66%
29.29%
28.21%
27.58%
26.83%
80
25.06%
29.66%
29.32%
28.29%
27.73%
27.07%

100
23.05%
27.91%
27.58%
26.57%
25.99%
24.76%
100
23.39%
27.91%
27.60%
26.69%
26.16%
25.57%

improved by about 1% for the mAP at different K. GR can
also makes the training more stable, as shown in Fig. 6.
Neighbor model also obtains excellent performance which
proves that it is beneficial to exploit the neighborhood structure
of the training data for the task of video retrieval. SSVH
achieves the best performance compared with other combinations, which indicates that each component contributes to
the good performance of SSVH. 3) Besides, we also construct
the similarity matrix using the labels. Obviously, our model
with human labels significantly outperforms our SSVH with
the unsupervised neighborhood structure, which means that we
can improve the accuracy of predicted neighbor similarity to
enhance our performance.
0.5

Fig. 6. The convergence study of our method on the FCVID dataset. FB:
Forward+Backward reconstruction, FB + GR: FB + Global Reconstruction.
(code length=256)

top20
top40
top60
top80
top100

dataset. While it is impossible to test all the combinations due
to the space limit, we focus on the following aspects: 1) Is the
hierarchical structure better than stacked LSTM? 2) What is
the effect of each component? and 3) What is the performance
of our neighborhood structure compared with human labels?
To achieve this, we report the mAP@K (K = 20, 40, 60,
80, 100) results of different combinations in Table.I. We can
observe that: 1) Compared to SSTH, a stacked RNN structure
which also uses forward and backward reconstructions, our
hierarchical structure (FB) obtains superior performance with
less computational cost. 2) Using FB only, our method can
achieve promising results, and global loss can further improve
the performance. By adding GR to FB, the performance is

mAP

0.4

0.3

0.2

0.1
0

0.0001

0.001

0.01

0.1

1

6

Fig. 7. Performances of different λ on FCVID (code length=256)

2) Trade off between Neighbor Loss and Reconstruction
Loss: The hyper-parameter λ in Eq.22 is crucial in our

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9

SSTH
Submod
DH
ITQ
MFH
SSVH

0.15

0.02

0.10

0

20

40
60
TopK

80

0.0

100

0

(a) FCVID 8 bits

40
60
TopK

80

100

0.0

0.1

0.3

20

40
60
TopK

80

0.2

0.0

100

0.1
0

20

40
60
TopK

80

0.0

100

20

40
60
TopK

80

100

0.4

0.10

SSTH
Submod
DH
ITQ
MFH
SSVH

0.3

mAP

SSTH
Submod
DH
ITQ
MFH
SSVH

0.15

mAP

mAP

0

(f) FCVID 256 bits

0.20

0.10

0.3

(e) FCVID 128 bits
SSTH
Submod
DH
ITQ
MFH
SSVH

100

0.2

(d) FCVID 64 bits
0.15

80

SSTH
Submod
DH
ITQ
MFH
SSVH

0.4

0.1
0

40
60
TopK

0.5

mAP

0.2

20

(c) FCVID 32 bits
SSTH
Submod
DH
ITQ
MFH
SSVH

0.4

mAP

SSTH
Submod
DH
ITQ
MFH
SSVH

0.3

0

0.6

0.5

0.4

mAP

20

(b) FCVID 16 bits

0.5

0.0

0.2
0.1

0.05
0.0

SSTH
Submod
DH
ITQ
MFH
SSVH

0.3

mAP

SSTH
Submod
DH
ITQ
MFH
SSVH

mAP

mAP

0.4

0.20

0.04

0.2

0.05

0.1

0.05
0.0

0

20

40
60
TopK

80

0.0

100

0

(g) YFCC 8 bits

80

0.0

100

0.1

SSTH
Submod
DH
ITQ
MFH
SSVH

0.2
0.1

20

40
60
TopK

80

100

0.0

40
60
TopK

80

100

SSTH
Submod
DH
ITQ
MFH
SSVH

0.3

mAP

0.2

20

0.4

0.3

mAP

SSTH
Submod
DH
ITQ
MFH
SSVH

0

0

(i) YFCC 32 bits

0.4

0.3

mAP

40
60
TopK

(h) YFCC 16 bits

0.4

0.0

20

0.2
0.1

0

20

40
60
TopK

80

100

0.0

0

20

40
60
TopK

80

100

(j) YFCC 64 bits
(k) YFCC 128 bits
(l) YFCC 256 bits
Fig. 8. Performance (mAP@K) of different video hashing methods with different code lengths. The first two rows are the results for the FCVID dataset, and
the bottom two rows are for the YFCC dataset.

method, which balances the neighborhood similarity loss and
the reconstruction loss of the training videos. Therefore, we
tune the parameter of λ from 0, 10−4 , 10−3 , 10−2 , 10−1 , 1 and
show the performance in Fig.7. The curves in Fig.7 shows how
the mAP at top20, top40, top60, top80, and top100 varies
with respect to λ. When λ = 0 and λ = 1, i.e., only the
neighborhood similarity loss or reconstruction loss is used,
SSVH cannot achieve the best performance. Instead, the best
performance is obtained when λ = 10−3 , which indicates that
it is necessary to use both information and the neighborhood

structure loss contributes more to the performance.
3) Effect of K1 and K2 in Neighborhood structure: In this
subsection, we evaluate mAP@K of different combinations of
K1 and K2 on the FCVID dataset. We tune both K2 and K1
from 5, 10, 20, 30, 40, 50, and the results are shown in Tab.
II. When K1 or K2 is relatively high (e.g.,50 ), the worst
performance is achieved. When both K1 and K2 are set as
10, best performance is achieved. If K1 or K2 is set to a
small number, there are not enough neighboring information
to preserve; while if K1 or K2 is set to a large number, then

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10

Top-5 Retrieval Result
Guitar Performance

SSVH

Ski Slope

SSVH

SSTH

SSTH
Tornado

Patio

SSVH

SSVH

SSTH

SSTH
Making Hotdog

Pantry

SSVH

SSVH

SSTH

SSTH

Fig. 9. The retrieval results of 256 bits when using SSVH and SSTH on FCVID and YFCC. Green border means correct retrieval result and Red border
means incorrect retrieval result. Left: FCVID; Right: YFCC

TABLE III
T HE RESULT COMPARISON OF DIFFERENT ACTIVATION FUNCTIONS ON
FCVID DATASET. p(h) DENOTES APPROXIMATED SGN FUNCTION DEFINED
IN E Q .28, tanh(h) DENOTES TANH FUNCTION . M AP IS CALCULATED
USING TOP 20 RETRIEVAL RESULTS .

tanh(h)
p(h)

64bits
24.87%
26.12%

128bits
31.58%
33.65%

256bits
35.84%
39.25%

similar videos categories will be considered as one category.
4) Comparison with different approximate activation functions: We have different ways to approximate sgn function.
One is p(h) as we illustrate in Sec.III-D and the another is
tanh. In this subsection, we compare these two activation
functions on FCVID dataset, and the experimental results are
shown in Tab.III. Note that the mAP is calculated using top20
retrieval results. From Tab.III, both tanh(h) and p(h) get
satisfactory performance. And p(h) performs slightly better
than tanh(h).
5) Cross-dataset evaluation comparison: Tab.IV lists the
cross-dataset performance of all the hashing methods following [1]. We can observe that all the methods suffer a

TABLE IV
C ROSS - DATASET M AP GAIN (%) BY H AMMING RANKING OF 256 BITS .
M AP IS CALCULATED USING TOP 20 RETRIEVAL RESULTS .
mAP-256bits
train:FCVID
test:YFCC
train:YFCC
test:FCVID

Submod

MFH

ITQ

DH

SSTH

SSVH

-33.8↓

-24.7↓

-4.76↓

-2.04 ↓

-11.6↓

-19.51↓

-20.3↓

2.38↑

-8.26↓

-3.93↓

7.58↑

8.45↑

performance drop when training on FCVID and testing on
YFCC. This indicates that the performance is related to the
scale of training dataset. When the scale of training dataset
decreases, the mAP will drop accordingly. Domain shift is
another possible reason for the performance decrease. On the
other hand, when we train on YFCC and test on FCVID, the
mAP is improved significantly than that training and testing
on FCVID. This demonstrate that for unsupervised hashing
models, more training data is beneficial for performance gain,
even though they are from different domains.
6) Comparison with State-Of-The-Arts on FCVID: Figure 8 shows the comparison of our SSVH with several
state-of-the-art video hashing methods. 1) Obviously, SSVH

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achieves the best performance at all bit lengths on the FCVID
dataset. Specifically, it outperforms the state-of-the-art method
SSTH by 9.6%, 9.3% 9.6%, 9.4% and 9.4% for mAP@K
(K=20,40,60,80,100) when the code length is 256. 2) The
advantage of our SSVH is obvious when the code length is
relatively large, e.g., 32, 64, 128 and 256 bits. However, our
method does not show the superiority over SSTH on short
codes such as 8 and 16 bits, even though it performs better than
the other baselines. One possible reason is that short codes
carry too less information for reconstructing the video content,
and preserving the neighborhood structure. 3) In general, with
the increase of code length, the mAP increases as well. For
example, the mAP@20 for our method increases from 16.2%
with code length of 8, to 37.9% with the code length of 256.
This indicates that the code length plays an important role
in video retrieval, and our method is suitable for longer hash
codes.
7) Comparison with State-Of-The-Arts on YFCC: For the
YFCC, the proposed SSVH consistently outperforms the other
methods as shown in Fig.8. 1) SSVH shows significantly better
performance compared with the baselines (ITQ, MFH, Submod, DH and SSTH) for different bits. Specifically, compared
to the best counterpart SSTH, the performance is improved by
13% in average in terms of mAP for 256 bits on YFCC. 2) An
interesting phenomenon is that the performance improvement
of SSVH is more significant for short codes such as 8 or 16
bits, which is different from the FCVID dataset. 3) Similar to
the results on the FCVID dataset, the mAP increases for with
the increase of code length. SSTH is a strong competitor when
the code length is 64, 128 and 256. The performance gap of
between our method and SSTH becomes marginal in terms
of mAP@60, mAP@80 and mAP@100. Because the training
strategy of YFCC is different from FCVID and we ignore lots
of similarity information of video pairs due to the limitation
of dataset scale.
F. Qualitative Results
The qualitative results are shown in Fig. 9. The left results
are obtained from the FCVID dataset, while the right results
are obtained from the YFCC dataset. In this sub-experiments,
both SSTH and SSVH generate 256 bits hash codes. In addition, videos marked with green indicate correct results, while
videos marked with red are wrong results. From Fig. 9, we can
see that in general, SSVH can obtain better results. Given two
queries “Guitar Performance” and “Ski Slope”, both SSTH and
SSVH obtain correct top5 retrieval videos. This indicates that
modeling temporal information is beneficial to discriminate
concepts that involve human actions. More interestingly, we
can observe that SSVH consistently outperforms the SSTH on
both FCVID and YFCC datasets. This indicates that SSVH is
able to obtain better temporal information for video hashing
than SSTH. As another example illustrated in Fig. 9 (i.e.,
“Tornado” in FCVID and “Patio” in YFCC), it seems that
capturing visual appearances is sufficient for retrieving them.
This indicates that both SSVH and SSTH are also powerful
for video categories that are not likely to be distinguished by
temporal information. We also illustrated some failure cases

11

for both methods. Two examples is the food related events like
“Making Hotdog” in FCVID dataset and “Pantry” in YFCC
dataset. Both methods cannot distinguish these actions from
similar videos e.g., “nail paining”.
V. C ONCLUSION
In this paper, we have extend the novel unsupervised deep
hashing method (SSTH), named self-supervised video hashing
(SSVH). To the best of our knowledge, SSVH is the first
method which learns the video hash codes by simultaneous
reconstructing the video contents and neighborhood structure.
Experiments on real dataset show that SSVH can significantly
outperform the others and achieve the state-of-the-art performance for video retrieval. However, we also find some
shortcomings of our model. We use pre-trained VGGNet to
extract video frame features which ignore consecutiveness and
temporal information of videos. In the future, we will consider
extracting motion feature to our model and fusing multiple
features to improve video retrieval performance.
VI. ACKNOWLEDGMENTS
This work is supported by the Fundamental Research Funds
for the Central Universities (Grant No. ZYGX2016J085), the
National Natural Science Foundation of China (Grant No.
61772116, No. 61502080, No. 61632007) and the 111 Project
(Grant No. B17008).
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